KR20170061447A - Method for Certificating Real Living Body Micro Sweating - Google Patents

Abstract

The present invention relates to a method of authenticating a micro-organism, and an actual micro-organism authentication method according to the present invention is a method executed by an apparatus having a sensor unit capable of sensing a fingerprint, A first step of generating N pieces of recognition data capable of distinguishing ridges and valleys of fingerprints by N (N &gt; = 3) valid sensing times in units of a specified sensing period ms (millisecond) (1? M <M) recognition data and the (m + 1) -th recognition data, recognizing the ridge region and pore positions included in the M (M? (M-1) data including m-th subtraction data generated by subtracting the sensed value of the m-th recognition data from the sensed value of the (m + 1) -th recognition data by correlating the ridge region of the recognition data with the porthole position, ) &Lt; / RTI &gt; A third step of reading out the generated difference data and discriminating at least one differential sphere region in which a difference value on a ridge area is greater than or equal to a threshold value specified in a specified difference base value; (I &gt; = 1) pieces of recognition data generated before the m-th piece of recognition data in which the difference point region is discriminated, and j (Mi) recognition data from the (n + 1) (mi? N <m + j) recognition data by matching the ridge area and the bony area included in each (i + j) (I + j-1) cumulative difference data obtained by subtracting (i + j-1) cumulative difference data obtained by subtracting If the cumulative difference value corresponding to the difference point region is specified A fifth step of numerically calculating whether the cumulative difference base value is within a predetermined effective living sweating time or not, a time required for the cumulative difference value to exceed a predetermined threshold, And a sixth step of authenticating the determined difference peculiar area as the actual micro-organism sweating if the validated living sweating time is within the effective sweating time.

Description

[0001] The present invention relates to a method for certifying a living body micro-

The present invention relates to a method and apparatus for detecting a fingerprint of a body part of a user including a fingerprint using a sensor unit capable of sensing a fingerprint, (M + 1) (1? M <M) recognition data by matching the fingerprint ridge region and the pore position of the M (M? 3) (I-1) -th difference data generated before the m < th &gt; recognition data in which the difference-in-error area is determined is discriminated from at least one difference- (I + j) pieces of recognition data existing between j pieces of recognition data and j (j? 0) pieces of recognition data generated after the m pieces of recognition data, 1) (mi? N <m + j) (I + j-1) cumulative difference data obtained by subtracting the (mi) recognition data (or the first recognition data) from the first difference data and generates a pattern in which the difference- It is actually authenticated with micro-bio-perspiration compared with the micro-perspiration pattern of perspiration.

With the recent activation of smart phones, a fingerprint module is embedded in a smart phone, and fingerprint recognition is used for operations or services requiring various security. It is expected that fingerprints will be more useful in the future than other living bodies because recognition process is convenient.

On the other hand, since fingerprint recognition is convenient, the possibility of forgery is easy. When fingerprint recognition is attempted by simulating fingerprints through materials having physical, chemical, and electrical characteristics similar to the epidermis of a human body, the conventional fingerprint recognition technology has a very difficult problem to exclude such simulated fingerprints. For example, when fingerprints are simulated using Gummi, which is very similar to the skin composition of the human body, it is very difficult to exclude such simulated fingerprints through conventional fingerprint recognition technology.

Examples of techniques for excluding such simulated fingerprints include Japanese Patent Application Laid-Open Nos. 2000-123143, 10-302047, 2000-194848, and 2000-172833, 10-2005-0051659 and the like.

However, in the technique disclosed in Japanese Patent Application Laid-Open No. 2000-123143 and Japanese Patent Application Laid-Open No. 10-302047, it is determined whether or not the subject is a living body by the current value of the subject, the capacitance, the electric resistance, , It is difficult to exclude a simulated fingerprint using a substance having chemical and electrical characteristics. In the technique disclosed in Japanese Patent Application Laid-Open No. Hei 10-370295, a counterfeit fingerprint is judged based on whether or not the electrostatic sensor responds. 2000-172833, it is difficult to exclude a simulated fingerprint using materials having physical, chemical and electrical characteristics similar to those of the skin of a human being, because the forgery fingerprint is judged by the frequency characteristic of impedance, which is an electrical characteristic . Korean Patent Laid-Open No. 10-2005-0051659 discloses a method of detecting a living body by using the presence or absence of sweat when scanning a sweep method of a fingerprint. However, in the case of a substance having physical, chemical and electrical characteristics similar to the skin of a human body It is difficult to exclude water if it is attempted to recognize it.

In order to solve the above problems, an object of the present invention is to provide a method and apparatus for sensing a body part of a user including a fingerprint by using a sensor part capable of sensing a fingerprint in N (N &gt; = 3) (M + 1) (1 < m &lt; M) by matching the fingerprint region of the M fingerprint region and the pore position of the M (M? 3) pieces of recognition data generated in the specified fine- (M-1) pieces of difference data by subtracting the m-th recognition data from the recognition data to discriminate at least one differential sphere region having a predetermined threshold value or more from the specified difference base value, (I + j) pieces of recognition data existing between j (j? 0) pieces of recognition data generated after the m-th recognition data and at least i (i? Areas and Goals (I + j-1) cumulative difference data obtained by subtracting the (mi) recognition data (or the first recognition data) from the (n + 1) (I + j-1) cumulative difference data to generate the cumulative difference value corresponding to the difference-in-error region from the designated cumulative difference base value over a predetermined threshold value, Time within a predetermined time, and authenticating the difference point region as an actual minute biofilter.

A method for authenticating an actual micro-organism according to the present invention is a method executed through an apparatus equipped with a sensor unit capable of sensing a fingerprint, wherein a sensor unit is provided for sensing a body part of a user at a predetermined sensing period ms (millisecond) 3) times of valid sensing to generate N pieces of recognition data capable of distinguishing ridges and valleys of the fingerprint from the generated recognition data; (1? M <M) recognition data and the (m + 1) recognition data and recognizes the ridge region and the pore position included in the (M? a second step of generating (M-1) pieces of difference data including m th subtraction data generated by subtracting the sensing value of the m-th recognition data from the sensing value of the (m + 1) The generated difference data is read A third step of discriminating at least one differential sphere region in which the difference value on the ridge region is equal to or greater than a specified threshold value from a specified difference base value; and determining, when at least one differential sphere region is discriminated in the m-th order data, (I + j) pieces of recognition data existing between j (j? 0) pieces of recognition data generated after the m-th recognition data and at least i (i? (I + j-1), which is obtained by subtracting the (mi) recognition data (or the first recognition data) from the (n + 1) (I + j-1) cumulative difference data to generate cumulative difference data corresponding to the differential positional area, The value takes more than the threshold specified Calculating a cumulative difference value of the difference differential value within a range of the effective living body sweating time when the cumulative difference value of the difference specific region is within a validated living body sweating time; And a sixth step of authenticating the difference point region with an actual minute biofilter.

According to the present invention, the sensing period may include a time period shorter than the set validated living body extinguishing time. Preferably, the fine flickering period is at least twice as long as the sensing period.

According to another aspect of the present invention, there is provided a method for authenticating a micro-organism, comprising the steps of: checking a contact state of a user's body part through the sensor unit; The method may further include the step of starting effective sensing.

According to another aspect of the present invention, there is provided a method for authenticating an actual micro-organism, comprising the steps of: sensing a user's body part in contact with the sensor unit in a predetermined sensing period ms; And starting acquiring the sensing data.

According to another aspect of the present invention, there is provided a method for authenticating a micro-organism, comprising the steps of: confirming disconnection of a body part during effective sensing of a user's body part in contact with the sensor part in a specified sensing period ms; Determining whether a minimum sensing time set for authenticating a living body sweating has elapsed from the time when effective sensing of the body part is started at the time of confirming the contact release, and ending the living body sweating authentication procedure when the minimum sensing time has not elapsed And performing the steps of

According to another aspect of the present invention, there is provided a method of authenticating a micro-organism, comprising the steps of: confirming an elapsed sensing time from a time point at which valid sensing starts at a specified sensing period ms in a user's body part contacting the sensor unit; Determining whether the time exceeds a maximum sensing time set for authenticating the biohazard, and performing a procedure for stopping or resuming effective sensing of the body part when the maximum sensing time is exceeded have.

According to the present invention, the second step includes the steps of: checking a ridge pattern of the m-th recognition data and the (m + 1) -th recognition data; 1) matching the recognition data.

According to the present invention, the second step includes the steps of: identifying a bone pattern of the m-th recognition data and the (m + 1) -th recognition data; 1) matching the recognition data.

According to the present invention, the second step includes the steps of: identifying a minutia on the ridge pattern of the m-th recognition data and the (m + 1) -th recognition data; And matching the (m + 1) -th recognition data.

According to the present invention, the second step includes the steps of: determining a base sensing value of a ridge area included in the generated recognition data; and determining S (S? 3 Recognizing at least s (3? S? S) sensing value singularity regions of the identified S sensing value singularity regions as s positions of portholes .

According to the present invention, the second step includes the steps of: confirming a geometrical relationship formed by s (s? 3) pores existing in the ridge area of each recognition data; Matching the position of the pore of the (m + 1) -th recognition data with the position of the pore of the (m + 1) -th recognition data.

According to the present invention, the second step includes the steps of: identifying minutiae points on the ridge pattern of the m-th recognition data and the (m + 1) -th recognition data; Matching the position of the mth recognition data with the position of the porthole of the (m + 1) -th recognition data based on the geometrical relationship of the pores; . &Lt; / RTI &gt;

According to the present invention, the actual micro vivo authentication method calculates a correction coefficient for matching the base sensing value of the ridge area of the (m + 1) -th recognition data with the base sensing value of the ridge area of the m-th recognition data Further comprising the steps of:

The second step may include generating the m-th order data by subtracting the sensing value of the m-th recognition data from the sensing value of the (m + 1) -th recognition data based on the correction coefficient.

According to the present invention, the actual micro-biomimetic authentication method may further include calculating an average of difference values of the generated difference data with respect to the ridge region and determining the average of the difference values as a difference base value with respect to the ridge region.

According to the present invention, the actual microscopic bioavailability authentication method may further comprise: calculating a difference value distribution for the ridge area of the generated difference data, and comparing a value belonging to the specified distribution area on the difference value distribution with a difference base value As shown in FIG.

According to the present invention, the fourth step may include the steps of confirming a ridge pattern of the (mi) recognition data (or the first recognition data) and the (n + 1) And matching the (mi) recognition data (or first recognition data) with the (n + 1) recognition data.

According to the present invention, the fourth step may include the steps of: identifying a bone pattern of the (mi) recognition data (or the first recognition data) and the (n + 1) And matching the (mi) recognition data (or first recognition data) with the (n + 1) recognition data.

According to the present invention, the fourth step may include the steps of: identifying minutiae on the ridge pattern of the (mi) recognition data (or first recognition data) and (n + 1) recognition data; And matching the (mi) recognition data (or the first recognition data) with the (n + 1) recognition data based on the (mi) recognition data.

According to the present invention, the real microscopic biometric authentication method comprises the steps of: acquiring a base sensing value for a ridge area of the (n + 1) recognition data and a base value for a ridge area of the (mi) recognition data (or first recognition data) (M + 1) -th recognition data (or the first recognition data) from the sensing value of the (n + 1) -th recognition data on the basis of the correction coefficient, and calculating the correction coefficient for matching the sensing value N) &lt; / RTI &gt; cumulative difference data by subtracting the sensed value of the n &lt; th &gt;

According to the present invention, the actual micro vivo authentication method further comprises calculating an average of cumulative difference values for the ridge region of the generated cumulative difference data and determining the average of cumulative difference values for the ridge region as the cumulative difference value .

According to the present invention, the actual micro vivo authentication method further comprises: calculating an accumulated difference value distribution for the ridge area of the generated cumulative difference data; and calculating a value belonging to the designated distribution area on the cumulative difference value distribution, As the cumulative difference base value.

According to the present invention, when the at least one differential region is not distinguished using N recognition data, the actual micro-biomimetic authentication method further validates L (L &gt; = 1) times in a predetermined sensing period ms unit (N + L) pieces of recognition data can be used to discriminate the difference point region.

According to the present invention, the ridge area is used as an identification means for identifying the user, and the effective living body swallowing can be used as authentication means for authenticating that the user is an actual user.

According to the present invention, by authenticating whether a fingerprint identified through a fingerprint recognition and matching process is obtained by sensing a body part of an actual user, it is possible to use a substance having physical, chemical and electrical characteristics similar to the skin of a human body, Even if fingerprints are copied, it is possible to authenticate all the types of fingerprints by authenticating only a live biopsy that is biologically reactive only to a living user, and it is advantageous to block the threat of hacking by forgery and falsification of all kinds.

According to the present invention, in order to authenticate a micro-organism sweating, even when a body part in contact with a sensor part moves while sensing a user's body part through a sensor part, And the threat of hacking by forgery and falsification is fundamentally blocked.

According to the present invention, there is an advantage in that fingerprint matching is used as identification means for identifying a user, and identification biometric authentication is used as an authentication means for authenticating fingerprint of an actual user, thereby simultaneously providing identification and authentication through one fingerprint .

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a view showing a cross-sectional structure of a skin against sweat glands formed on a human body;
2 is a view illustrating an optical image of a ridge, a bone, and a pore formed in a fingerprint of a human body.
FIGS. 3A to 3F are diagrams showing cross-sectional structures of a ridge, a bone, and a pore formed in a fingerprint of a human body.
Figs. 4A and 4B are views showing images obtained by optically photographing fine swellings of pores formed in a ridge of a fingerprint. Fig.
5A to 5C are diagrams showing sweating patterns of the human body.
6A and 6B are views showing a cross-sectional structure for sensing a ridge, a bone, and a pore formed in a fingerprint of a human body.
7 is a functional block diagram of an apparatus for authenticating an actual living body sweating according to an embodiment of the present invention.
8A to 8C are diagrams illustrating the relationship between the recognition data and the differential point region according to the method of the present invention.
9 is a diagram showing the relationship between the sensing period and the effective living body swallowing time according to the method of the present invention.
10 is a diagram illustrating a process of authenticating an actual living body sweating according to an embodiment of the present invention.

The operation principle of the preferred embodiment of the present invention will be described in detail with reference to the accompanying drawings and description. It should be understood, however, that the drawings and the following detailed description are exemplary and explanatory and are intended to provide further explanation of the invention, and are not to be construed as limiting the present invention.

In other words, the following embodiments correspond to the preferred embodiment of the preferred embodiment of the present invention. In the following embodiments, a specific configuration (or step) is omitted, or a specific configuration (or step) (Or steps), or an embodiment that incorporates functions implemented in more than one configuration (or step) into any one configuration (or step), a particular configuration (or step) It will be apparent that the present invention is not limited to the embodiments described below. Therefore, it should be clearly stated that various embodiments corresponding to subsets or combinations based on the following embodiments can be subdivided based on the filing date of the present invention.

In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear. The terms used below are defined in consideration of the functions of the present invention, which may vary depending on the user, intention or custom of the operator. Therefore, the definition should be based on the contents throughout the present invention.

As a result, the technical idea of the present invention is determined by the claims, and the following embodiments are merely means for effectively explaining the technical idea of the present invention to a person having ordinary skill in the art to which the present invention belongs Only.

FIG. 1 is a view showing a skin cross-sectional structure for the sweat glands formed on the human body.

There are two types of sweat glands in the human body: eccrine sweat glands and apocrine sweat glands. Apocrine sweat glands are sweat glands that are formed with hair and sebaceous glands. They are distributed in the armpits, outer ear (outer ear canal), eyelids, etc., and the eczrine sweat glands are sweat glands distributed throughout the skin except the glans part of the body, Among them, eccrine sweat glands are mainly distributed in the palms, soles, underarms, and forehead skin. The sweat produced from each sweat gland migrates along the sweat glands and sweats through the sweat pores. The size of the sweat glands is about 50 times smaller than the thickness of the hair (about 50 μm to 70 μm).

2 is a view illustrating an optical image of a ridge, a bone, and a pore formed in a fingerprint of a human body.

Ridges of the fingerprint are formed along the positions of the sweat glands in fingers or toes of the body parts of the human body, and valleys are formed between ridges and ridges. That is, the ridge of the fingerprint is a ridge of the skin along the sweat glands formed on the finger or the toe. Some rinse sweat glands are distributed mainly in ridge of fingerprints, but some of apocrine glands may be formed.

Male ridges are formed at intervals of 200 μm to 850 μm and average distances are measured at about 460 μm. In the case of females, ridges are formed at intervals of 200 μm to 750 μm, and the average distance is measured to be about 410 μm. Also, the distance between pores is formed at intervals of 200 mu m to 1500 mu m, and the average distance is measured to be about 830 mu m. However, the above figures are average statistics and the actual distance may vary from person to person.

FIGS. 3A to 3F are views showing cross-sectional structures of ridges, bones, and pores formed in a fingerprint of a human body.

In more detail, FIGS. 3A to 3F show a short structure when the fingerprint of the human body is directed upward. The pores formed on the ridge of the fingerprint can be formed in various shapes such as circular, elliptical, and fusiform, and the cross section of the pores has a crater shape. At this time, the diameter of the crater is within 200 占 퐉.

When the sweat produced in the sweat glands starts to swell, the sweat produced in the sweat glands moves along the sweat gang as shown in FIG. 3b, and sweats as shown in FIGS. 3c to 3e to fill the craters of the swallow gang first. According to the definition of the present invention, the sweat which is sweated and is held in the crater of the pore hole as shown in Figs. 3c to 3e is defined as &quot; micro sweat &quot;. According to another definition of the present invention, fine sweat can be defined as perspiration sweating less than 1 μL.

If the sweat generated in the sweat glands is further sweated in the sweated minute sweat state as shown in FIGS. 3c to 3e, the sweated sweat flows over the crater of the sweat hole as shown in FIG. 3f. The present invention defines sweat flowing over a crater of a pore as "wet sweat" as shown in FIG. 3f. Preferably, the wet sweat first flows along the ridge and then overflows in the bone direction. Usually, the expression "sweat" means wet sweat.

On the other hand, according to the experiment of the applicant, it is unusual for the perspirated fine sweat to grow as the wet sweat as shown in FIG. 3f, as shown in FIGS. 3c to 3e, unless a specific perspiration induction situation occurs. In the human body, about 500 to 900 pores are arranged irregularly per 1 cm 2. In some of these pores, fine sweat is generated irregularly as shown in FIGS. 3c to 3e. However, most of the fine sweat is only accumulated in the crater of the pore, as shown in FIG. 3c, FIG. 3d or FIG. 3e, and disappears, and it is rare to grow wet sweat as shown in FIG. 3f unless a special sweat induction situation occurs. According to the definition of the present invention, the perspiration of fine sweat which is present in the crater of a pore as shown in Fig. 3c, Fig. 3d or 3e is defined as &quot; fine sweating &quot;. That is, in the present invention, micro-sweating can be defined as sweating in the form of being swollen in the crater of the pore as shown in FIG. 3c, FIG. 3d or FIG. 3e and then disappearing without growing with wet sweat.

In addition, according to the repeated experiments of the applicant, it has been found that microscopic sweating occurs very irregularly (that is, unpredictable in which pores it swells) and in some milligrams, in milliseconds, in figures 3b to 3c or 3d or 3e As shown in FIG. 3B, and then the fine perspiration disappears as shown in FIG. 3B. The reason why the fine perspiration persists in ms units as shown in FIG. 3B, FIG. 3C, FIG. 3D or FIG. 3E and then disappears as shown in FIG. 3B is because very small amount of fine perspiration sweated in a very small amount or less evaporates at the same time, It is assumed that the fine sweat is sucked into the state of FIG. 3b by a certain biological reaction. On the other hand, in the case of some other minute sweating, it has been confirmed that the sweating state is maintained for a predetermined time (for example, ms units to one second) as shown in FIG. 3C, FIG. 3D or FIG. However, the fine sweat which has been maintained for a certain period of time is mostly extinguished to the state of FIG. 3b unless a specific perspiration induction state occurs, and it can be confirmed that only a part of the fine sweat which has been maintained for a certain period of time grows as wet sweat.

FIGS. 4A and 4B are views showing images obtained by optically photographing fine swellings of pores formed in a ridge of a fingerprint. FIG.

More specifically, FIGS. 4A and 4B are optical close-up images taken in units of 500 ms (0.5 second) so as to optically discriminate fine sweating. For reference, there was no sweating induction between Figures 4a and 4b.

Comparing the blue square area in FIGS. 4a and 4b, the pores in the pores in FIG. 4a are fine-grained in units of ms, and fine sweat fine-sweated in ms units can be identified as shown in FIG. 4b.

4A and FIG. 4B, it can be seen that the fine sweat in a state of fine sweating in FIG. 4A maintains fine sweat as shown in FIG. 4B even after the lapse of ms.

On the other hand, when comparing the green square areas in FIGS. 4A and 4B, it can be seen that the fine sweat which has been slightly sweated in FIG. 4A has disappeared as shown in FIG. 4B after lapse of ms.

5A to 5C are diagrams showing sweating patterns of the human body.

5B shows sweating baton of fine sweat which is maintained for a predetermined time and FIG. 5C shows sweat baton of fine sweat which persists after sweating in units of ms. Fig. And a sweating baton capable of growing.

According to the experiment of the applicant, it was confirmed that when a sweat generated from sweat glands of a human body is sweated, there is a pattern sweating a specific amount of sweat rapidly in ms units at the beginning of sweating as shown in Figs. 5A to 5C. In particular, even when fine sweat persists through sweat pores of pores formed in a ridge of a fingerprint, it has a pattern as shown in Figs. 5A to 5C.

5A shows a sweating pattern of fine sweat which persists the sweat generated in the sweat glands of the human body in minute increments and disappears in ms units. Referring to FIG. 5a, sweating of fine sweat was observed to occur rapidly in ms units at the beginning of sweating, whereas sweating of sweating occurred more slowly than sweating. This is because the time required for evaporation or sweating of the fine sweat through the sweatpin is slower than the sweating time.

FIG. 5b shows a sweating pattern of fine sweating which keeps a minute sweating state for a predetermined time (for example, ms units to one second) or more. Referring to FIG. 5B, sweating of fine sweat occurs rapidly in the unit of ms in early stage of sweating, and the time for accommodating sweat in the sweat hole is maintained for a predetermined time or more. In this case, the fine perspiration is maintained for a predetermined period of time. That is, the sweating phenomenon is not stopped through the sweatpin (if sweating through the sweatpin stops, the sweating pattern is similar to that of FIG. 5a) It seems to be continuing. The sweating pattern shown in FIG. 5B is sweated in the previous period due to the perspiration pattern periodically shown in FIG. 5A, and is accommodated in the sweat pores and evaporated or swollen at the same time. The amount of perspiration remaining in the sweat pores, It is judged that the sum of the amounts of sweat produced does not exceed the accommodation space of the pores. If sweating through the sweatpipe is stopped at any moment, the fine sweat will disappear similar to the disappearing pattern of FIG. 5a.

According to the method of the present invention, when the body part of the human body is kept in contact with the sensor part 705, the evaporation rate of the sweat sweated by the contact between the body part and the sensor part 705 decreases A pattern may be generated in which the minute sweating state is maintained for a predetermined time or longer as shown in FIG. 5B.

FIG. 5c shows a sweating pattern of fine sweating which can be grown by wet sweat from fine sweat. Referring to FIG. 5c, sweating of micro-sweat occurs sharply at the beginning of sweating and in units of ms. Similar to FIG. 5b, the sweating time is maintained for a certain period of time or longer. The sweating process seems to be continuing. Then, as soon as the amount of sweat contained in the pores exceeds the acceptable volume of the pores, the sweat becomes wet sweat and flows along the ridges or flows into the bone. According to the applicant's experiment, when the fine sweat contained in the pore grew by wet sweat, it was confirmed that the sweat swelled suddenly in ms in the state of fine sweat. This is because the perspiration sweated through the sweat pipe is gradually filled with the sweat pores and does not grow as wet sweat. Instead of repeating the sweating pattern as shown in Fig. 5a, the amount of perspiration sweated through the sweatpin during the period and the amount of perspiration It is judged that the sweat flows suddenly when it exceeds the acceptable volume in the corresponding pores.

On the other hand, the sweating patterns shown in Figs. 5A to 5C are not limited to fine sweat, but are equally applicable to sweating wet sweat immediately in an untreated state. That is, the criterion for distinguishing whether the sweat sweating from the human body is fine sweat or wet sweat is determined according to whether the sweat amount sweating rapidly in ms units through the sweat pipe is smaller or larger than the accommodation space of the pores, (Particularly sweating in the pore formed in the ridge of the fingerprint) has sweating pattern that rapidly sweats a certain amount of sweat in ms at the beginning of sweating as shown in Figs. 5a to 5c.

Therefore, as shown in FIGS. 5A to 5C, the sweating pattern rapidly sweating in units of ms in the early stage of sweating can be defined as a unique feature of sweating through the pores of the human body.

6A and 6B are views showing a cross-sectional structure for sensing a ridge, a bone, and a pore formed in a fingerprint of a human body.

6a shows a cross-sectional structure for sensing fingerprint ridges, valleys and pores in Fig. 3a using a sensor unit 705 having an electrostatic sensor for sensing capacitance, Fig. 6b is a cross- And a fingerprint ridge, a bone and a pore of the finger in Fig. 3d are sensed using a sensor unit 705 having an expression sensor.

Referring to FIGS. 6A and 6B, the ridges of ridges, bones, and pores of the fingerprint contact (or come close to) the sensor unit 705. Therefore, when the sensor unit 705 is made of an electrostatic sensor, the electrostatic sensor acquires a sensing value closest to the capacitance of the human body with respect to the ridge. On the other hand, as compared with the ridge, the ridge is spaced apart from the sensor unit 705 by a predetermined distance or more. Therefore, when the sensor unit 705 is made of an electrostatic sensor, the electrostatic sensor acquires a sensing value of a capacitance sufficiently contrasted with the capacitance of the ridge with respect to the valley. The sensor unit 705 can acquire sensing data capable of distinguishing ridges of fingerprints from each other according to the sensed values of ridges and bones.

On the other hand, pit-shaped pores having a diameter of about 200 mu m are irregularly arranged at intervals of about 830 mu m on the ridge. Therefore, in order to sense pores having a diameter of about 200 μm, the sensor resolution of the sensor unit 705 (for example, the size of one pixel of the electrostatic sensor provided in the sensor unit 705) is preferably 50 μm or less. When the sensor resolution is 50 μm or less, the sensor unit 705 can effectively sense the pores through at least one to four sensors.

As shown in FIG. 6A, in an untreated state where sweat does not swell, a void is formed in the pore on the fingerprint ridge. Therefore, when the sensor unit 705 is an electrostatic sensor, the electrostatic sensor can obtain a sensing value corresponding to a sensing value between the sensing value of the ridge and the sensing value of the ridge relative to the pore of FIG. 6A.

As shown in FIG. 6b, when perspiration of fine sweat occurs, the perspiration sweat contains salt, amino acid, cell, etc., and thus the electrostatic capacity of the perspiration may be larger than the electrostatic capacity of the human body. Therefore, when the sensor unit 705 is made of an electrostatic sensor, the electrostatic sensor can obtain a sensing value corresponding to a capacitance larger than the sensing value of the ridge with respect to the pore of FIG. 6A.

7 is a functional block diagram of an apparatus 700 for authenticating an actual living body sweating according to an embodiment of the present invention.

More specifically, FIG. 7 illustrates an example in which the body part of the user including the fingerprint is sensed N (N? 3) times in units of a predetermined sensing period ms (millisecond) using the sensor unit 705 capable of sensing the fingerprint, (M + 1) (1? M <M) recognition data by matching the fingerprint ridge area and the porthole area of the M (M? 3) (M-1) pieces of difference data by subtracting the m-th recognition data to discriminate at least one differential sphere region having a predetermined threshold value or more from the specified difference base value, (I + j) pieces of recognition data existing between at least i (i? 1) pieces of recognition data and j (j? 0) pieces of recognition data generated after the mth recognition data, (N + 1) (m- (i + j-1) th accumulated difference data obtained by subtracting the (mi) recognition data (or the first recognition data) from the recognition data, ) Cumulative difference data, and when the cumulative difference value corresponding to the difference point region is within a predetermined effective living body fluctuation time within a predetermined cumulative difference base value, It is to be understood by those skilled in the art that the functional configuration of an apparatus 700 for verifying body fluids is illustrated and described herein with reference to and / It will be appreciated that the present invention encompasses all manner of practicing the invention, including but not limited to, any method (s) (e.g., some of the constituent parts omitted, or subdivided, Only an exemplary method illustrated in surface 7 shall not be limited to that technical feature.

The device 700 of the present invention collectively refers to a configuration for authenticating actual living body sweating through pores formed on a ridge of a fingerprint using a sensor unit 705 capable of sensing a fingerprint, (Or chip) type, a distributed system including various devices and servers on a network, and the like.

According to a first embodiment of the present invention, the apparatus 700 of the present invention can be implemented in the form of various apparatuses used by a user. For example, the device 700 may be implemented in the form of a terminal (e.g., a smart phone, a mobile phone, a tablet PC, a notebook PC, etc.), a recognition device, an authentication device, a door lock device, 7 may be implemented in the form of H / W, S / W, or a combination thereof mounted on each device.

According to the second embodiment of the present invention, the apparatus 700 of the present invention can be implemented in the form of a module (or a chip) mounted on various apparatuses used by a user. For example, the device 700 may be implemented in the form of a module (or chip) embedded in various devices such as a terminal, a recognition device, an authentication device, a door lock device, etc. used by a user, The configuration unit may be implemented in a form of H / W or S / W or a combination thereof mounted on the module (or chip). Depending on the implementation method, some of the functional components shown in FIG. And the other part may be implemented on the device side on which the module (or chip) is mounted.

According to the third embodiment of the present invention, the apparatus 700 of the present invention can be distributedly implemented through various devices used by users and servers on the network. For example, the device 700 may include various devices such as a terminal, a recognition device, an authentication device, a door lock device, and the like, which are used by the user, and a server on the network. And other parts may be implemented in a server on the network.

According to the fourth embodiment of the present invention, the apparatus 700 of the present invention can be implemented in a form of partially combining two or more embodiments among the first to third embodiments. That is, the present invention is not limited to the method of implementing the device 700 or the embodiment, and it is obvious that the present invention belongs to the scope of the present invention if the functions included in the claims are implemented in any form of function.

The apparatus 700 of the present invention includes a sensor unit 705 capable of sensing fingerprints in a hardware manner. The sensor unit 705 may be any sensor capable of recognizing a fingerprint, such as an electrostatic sensor, an optical sensor, an ultrasonic sensor, or the like. If the sensor 705 is a sensor capable of sensing fingerprints, It is clear that the sensor portion 705 of the present invention is available and belongs to the scope of the right. Hereinafter, the characteristics of the present invention will be described using an embodiment using a sensor unit 705 having an electrostatic sensor for sensing a capacitance. However, it should be clear that embodiments of the present invention are by no means limited to the manner in which electrostatic sensors are used.

According to the method of the present invention, the sensor unit 705 preferably has a resolution of 50 μm or less (for example, one pixel of the electrostatic sensor).

Referring to FIG. 7, the apparatus 700 includes N sense (S) sensing N (N &gt; = 3) valid sensing units of a user's designated specific body part through a predetermined sensing period ms A fingerprint recognition unit 715 for extracting feature points on a ridge pattern corresponding to a fingerprint of a user using the sensing data according to an embodiment of the present invention; And a fingerprint matching unit 725 that authenticates the extracted feature points using a fingerprint template of a user previously stored in a designated storage area have. Meanwhile, the fingerprint recognition unit 715, the fingerprint storage unit 720, and the fingerprint registration unit 725 may be omitted according to the embodiment, and thus the present invention is not limited thereto.

The sensing processing unit 710 acquires N sensing data obtained by sensing the user's body part N times in a predetermined sensing period ms through the sensor of the sensor unit 705. Preferably, the sensing period includes a time period shorter than the effective living body exhalation time set for the actual living body authentication, as illustrated in FIG. 9. For example, if the effective living body extinguishing time is set to 100 ms, the sensing period should include a time period (e.g., 50 ms, 20 ms, 10 ms, etc.) shorter than 100 ms.

According to the embodiment of the present invention, the sensing processing unit 710 senses the N sensed data (N sensing data) sensed N times in a unit of sensing cycle ms in which the user's designated body part can be sensed in the shortest period within the allowable range of the sensor unit 705 Can be obtained. For example, when the user's designated body part can be sensed in units of 10 ms on the basis of the performance of the sensor unit 705, the sensing processor 710 senses the user's designated body part N times N sensing data can be obtained. However, the sensing period is not limited to the shortest allowable period of the sensor unit 705, and may be variously determined according to the performance of the sensor unit 705.

According to the embodiment of the present invention, the sensing processing unit 710 senses N sensing data obtained by sensing the same body part N times in a predetermined sensing period ms while the body part of the user is in contact with the sensor unit 705 . Preferably, the sensing processing unit 710 acquires sensing data for generating image data of pixels. For example, one pixel of the image corresponding to the sensing data sensed by the sensing processing unit 710 is preferably 50 m or less.

According to the first sensing method of the present invention, the sensing processing unit 710 confirms the contact state of the user's body part through the sensor unit 705, and when the contact of the user's body part is confirmed, 705 to obtain N sensed data effective sensing in units of a sensing period ms.

On the other hand, it is relatively easy to copy fingerprints on the other hand, but it is very difficult to replicate the pores, and it is virtually impossible to simulate the sweating through the simulated pores. Nevertheless, someone in the future can try to simulate the fingerprints and pores of others, or simulate vital sweats through the simulated pores using expensive control equipment.

Therefore, according to the second sensing method of the present invention, the sensing processing unit 710 confirms the contact state of the user's body part through the sensor unit 705 and does not start sensing immediately in the contact state of the body part, In the state where the contact of the body part is confirmed, the sensing data obtained by valid sensing the user's body part in a unit of a predetermined sensing period ms from an unspecified arbitrary time is started, so that even an attempt to simulate the living body sweating can be excluded.

According to the third sensing method of the present invention, the sensing processing unit 710 checks the contact state of the user's body part through the sensor unit 705, and determines the user's body part contacting the sensor unit 705 The sensed data sensed in units of sensing cycles ms is acquired and the sensed data sensed from an arbitrary unspecified time in the sensed sensing data is acquired as effective sensing data so that even an attempt to simulate the living body sweating can be omitted have.

According to the fourth sensing method of the present invention, the sensing processor 710 may combine at least two of the first to third sensing methods to obtain N sensed data that are sensed in a predetermined sensing period ms can do.

Meanwhile, the sensing processing unit 710 confirms whether or not the contact of the body part is released while the user's body part touching the sensor unit 705 is sensed effective for a specified sensing period ms. If it is determined that the body part is released from the contact, the sensing processor 710 sets the minimum sensing time (for example, 500 ms, 1 second, 2 seconds Etc.) has elapsed. If the contact of the body part is released in a state where the minimum sensing time has not elapsed, the sensing processor 710 terminates the biofeedback authentication process according to the present invention (for example, forcible stop, error processing, etc.) , It is possible to exclude unintended misrecognition.

Alternatively, the sensing processing unit 710 may check the elapsed sensing time from the time when effective sensing starts at a specified sensing period in units of a user's body part in contact with the sensor unit 705, and if the sensed sensing time is a living body sweating (E.g., 500 ms, 1 second, 2 seconds, etc.) set for authentication. If the effective sensing time exceeds the set maximum sensing time, the sensing processor 710 performs a procedure of stopping (e.g., stopping sensing or ignoring sensing data) or restarting the sensing of the body part, It is possible to exclude further attempts to simulate vital sweating. For example, if the time at which the effective sensing is started in the second or third sensing method is 250 ms after the contact of the body part and the maximum sensing time is 500 ms, the user can not recognize the body part in the sensor part 705 Even if the user is in contact for a long time, the time for acquiring the actual effective sensing data is only 250 ms to 750 ms after the contact of the body part, so that the attempt of simulating living body sweating can be more difficult.

Meanwhile, the fingerprint recognition unit 715 performs a process of preprocessing the sensing data obtained through the sensing processing unit 710 according to a specified fingerprint recognition algorithm. The fingerprint recognition unit 715 may apply a fingerprint recognition algorithm to any one of N sensing data obtained through the sensing unit 710 and may apply a fingerprint recognition algorithm to the N sensed data It is possible. Preferably, the fingerprint recognition unit 715 performs a smoothing process on the sensing data obtained through the sensing process unit 710, and the noise in the sensing process is removed through the smoothing process.

The fingerprint recognition unit 715 performs a preprocessing procedure for reading the corrected sensing data obtained through the sensing processing unit 710. [ Preferably, the fingerprint recognition unit 715 performs a binarization process, a thinning process, and a directionality extraction process for the acquired / corrected sensing data. The binarization procedure includes a procedure for distinguishing between a ridge portion and a valley portion on the sensing data in black and white. The thinning procedure includes a process of distinguishing the width of the ridge on the binarized sensing data by a line of a specified reference pixel (e.g., one pixel). The directional extraction procedure includes a procedure of extracting a relative direction of a ridge (e.g., a thin line) within the entire area of the binarized or thinned sensing data.

The fingerprint recognition unit 715 reads the preprocessed sensing data and performs a process of extracting minutiae points on the ridge pattern included in the sensing data. Preferably, the feature point includes at least one of a start point, an end point, a branch point, a center point, and a delta, and may be a merge, a loop, a bridge, a cross, , Triangle, Break, Spur, Ladder, Double Break, Break & Merge, Iceland, and more. have.

When the user's fingerprint is registered, the fingerprint storage unit 720 may generate a fingerprint template including the extracted feature points and store the fingerprint template in a designated storage area. According to an embodiment of the present invention, in order to register a fingerprint of a user, the sensor unit 705 senses the same part of the user's body (e.g., touches the same part of the body to the sensor unit 705, The fingerprint storage unit 720 may generate a fingerprint template including the union of the minutiae repeatedly extracted in the repeated process and store the fingerprint template in the designated storage area.

On the other hand, in the case of authenticating the user's fingerprint, the fingerprint matching unit 725 compares the fingerprint template stored in the designated storage area with the extracted minutiae through the fingerprint recognition unit 715 to check whether they match or not, A fingerprint matching result is generated.

Referring to FIG. 7, the apparatus 700 includes a recognition data generating unit 730 for generating sensed data by reading sensed values of respective sensing data obtained through the sensing processing unit 710, A data reading unit 735 for recognizing a ridge area and a pore position included in M (M? 3) pieces of recognition data generated in units of ms, A matching processor 740 for matching the ridge area and the pore position of the m (1? M <M) recognition data included in the recognition data and the (m + 1) (M-1) pieces of difference data including m-th difference data generated by subtracting the sensed value of the m-th recognition data from the sensed value of the matched (m + 1) 745, &lt; / RTI &gt; A read will be provided with a fine sweat determination unit 750 to determine the at least one primary singularity area occurred more than a specified threshold value specified at the car base the difference values on the ridge zone to the fine sweat.

The recognition data generation unit 730 reads the sensing value of each sensing data obtained through the sensing processing unit 710 in cooperation with the process of acquiring N sensing data through the sensing processing unit 710, And generates N pieces of recognition data capable of distinguishing the N pieces of recognition data.

According to the first recognition data generation method of the present invention, the recognition data generation unit 730 reads the sensed value of each sensed data to acquire a sensing value corresponding to the ridge region and a sensing value corresponding to the bony region It is possible to generate discrimination data which can be discriminated and corrected.

According to the second recognition data generation method of the present invention, the recognition data generation unit 730 reads the sensed value of each sensed data to obtain a sensed value corresponding to the ridged region and a sensed value corresponding to the target region The recognition data corrected so as to be distinguishable and corrected so as not to damage the sensing value corresponding to the pores arranged on the ridge area can be generated.

According to the third recognition data generating embodiment of the present invention, the recognition data generating unit 730 reads the sensed value of each sensed data to acquire the contrast of the sensed values, And the saturation can be generated.

According to the fourth embodiment of the present invention, the recognition data generation unit 730 reads the sensed value of each sensed data to acquire noise of the sensor included in the sensor unit 705 (For example, noise due to contamination of the surface of the sensor unit 705) generated in the sensing process through the sensor unit 705, and noise generated by the sensor unit 705 It is possible to generate recognition data by correcting at least one of noise caused by contamination of the approaching body part (e.g., noise due to moisture or dust in the body part contacting the sensor part 705).

According to the fifth recognition data generation embodiment of the present invention, the recognition data generation unit 730 can generate the recognition data corrected in a combination of two or more of the first to fourth recognition data generation embodiments. That is, the fingerprint recognition unit 715 performs correction such as smoothing, binarization, thinning, etc. to concentrate on extracting the minutiae points of the ridge pattern and undermining the sensing value of the ridge pores on the ridge, while the recognition data generation unit 730 The recognition data is generated by performing correction to improve the quality of the sensing data without damaging the pore sensing value on the ridge.

(Or after the recognition data is generated) by the recognition data generation unit 730, the data reading unit 735 reads the recognition data from among the N recognition data generated through the recognition data generation unit 730 (M &gt; = 3) pieces of recognition data generated in units of ms in fine detection phase, recognize ridge regions (or bony regions) of the selected pieces of recognition data, and recognize preexisting regions Recognize the location. Preferably, the data reading unit 735 can read the ridge area (or the bony area) included in the recognition data using the pattern recognition algorithm.

When the ridge area of the recognition data is recognized, the data reading unit 735 can recognize the position of the pores existing in the ridge area of each recognition data generated through the recognition data generation unit 730.

According to the embodiment of the present invention, the data reading unit 735 determines a basis sensing value of a ridge area included in each recognition data generated, and calculates a S sensed value S (S? 3) sensing value singularity regions, and then recognizing at least s (3? S? S) sensing value singularity regions of the identified S sensing singularity singularity regions as s pore positions.

According to the first base sensing value determination method of the present invention, the data reading unit 735 may determine the base sensing value of the ridge region by calculating a region distinguishing value for distinguishing the ridge region and the bony region. On the other hand, according to the method, the data reading unit 735 can determine the base sensing value of the ridge region by correcting the area discrimination value (for example, an arithmetic expression for correcting the error).

According to the second base sensing value determination method of the present invention, the data reading unit 735 may calculate the sensing value distribution of the ridge region, and may calculate a value belonging to the designated distribution region on the sensing value distribution, It can be determined as a sensing value. Meanwhile, according to the embodiment, the data reading unit 735 may correct the value belonging to the designated distribution area on the sensing value distribution to determine the base sensing value of the ridge area. For example, when the sensing value distribution of the designated area is a normal distribution, the data reading unit 735 reads a value belonging to the average (m) of the normal distribution or the standard deviation Can be determined as the base sensing value of the ridge area.

According to the third base sensing value determination embodiment of the present invention, the data reading unit 735 may calculate the sensing value average of the ridge region and determine the base sensing value of the ridge region. Meanwhile, the data reading unit 735 may correct the sensing value average to determine the base sensing value of the ridge region according to the method.

According to the fourth base sensing value determining embodiment of the present invention, the data reading unit 735 may partially combine at least two of the first through third base sensing value determining embodiments to perform the base sensing Value can be determined. For example, the data reading unit 735 may divide the recognition data into a designated region according to a specified rule, and determine a base sensing value for the ridge region for each of the divided regions. The body part contacting the sensor part 705 is not a two-dimensional plane structure but a three-dimensional structure. Therefore, when the body part is brought into contact with the sensor part 705 of the two-dimensional plane structure, the body part of the three-dimensional structure contacts the sensor part 705 of the two-dimensional plane structure, It is difficult to determine a single basis sensing value for the entire area of the recognition data because the distribution of contact forces is difficult to uniformly act on the entire body part. Even if the geometric shape of the sensor unit 705 is made to match with the body part of the three-dimensional structure, since the distribution of the force of contacting the body part with the sensor unit 705 is not always uniform, Determining a single baseline sensing value can be daunting. The data reading unit 735 may divide the recognition data into a designated region according to a specified rule and determine a base sensing value for the ridge region for each divided region. For example, the data reading unit 735 may divide the recognition data into a grid structure, a concentric circle structure, or a sector shape to determine a base sensing value for the ridge region for each divided region The first to third base sensing value determination embodiments may be applied to the respective regions. On the other hand, the data reading unit 735 identifies a gradation pattern of the sensing value included in the recognition data, divides the recognition data based on the gradation pattern, and outputs a base sensing value Can be determined. That is, since the distribution of the force of contacting the body part with the sensor part 705 is not always uniform, there is a gradation pattern in the sensing value between the part where the user applies more force and the part where less force is applied. The data reading unit 735 determines the gradation pattern of the sensing value, divides the gradation pattern into a predetermined number of steps, divides the recognition data on the basis of the gradation pattern, and determines the base sensing value for the ridge region have. Similarly, in this case, the first to third base sensing value determination embodiments may be applied to the respective regions.

Meanwhile, according to the first sensing value singularity discrimination embodiment of the present invention, the data reading unit 735 reads the first sensing value singularity from the base sensing value on the ridge area included in the designated area (for example, the entire area or the divided area) An area that is greater than or equal to a predetermined '+' contrast value can be identified as the sensing value singularity area. For example, when fine sweat is generated on the ridge-shaped pores as shown in FIG. 3E, the data reading unit 735 reads an area having a value greater than or equal to a predetermined '+' contrast value than the base sensing value on the ridge area, Area. &Lt; / RTI &gt;

According to the second sensing value singularity discrimination embodiment of the present invention, the data reading unit 735 reads an area of the ridge area included in the designated area of the recognition data, which is less than or equal to a preset '-' Sensing value singularity area. For example, when fine sweat is not generated on the ridge-shaped pores as shown in FIG. 3A or FIG. 3B, the data reading unit 735 reads an area of the ridge area that is less than a predetermined '-' And can be identified as the sensing value singularity region.

According to the third sensing value singularity discrimination embodiment of the present invention, the data reading unit 735 reads the third sensing value singularity in the area of the ridge area included in the designated area of the recognition data in the area less than the pre- An area including an area that is equal to or greater than the set '+' contrast value can be identified as the sensing value singularity area. For example, when a very small amount of fine sweat is generated on the ridge-shaped pores as shown in FIG. 3C, the data reading unit 735 sets the predetermined value in the region that is less than the pre-set '-' A region including a region equal to or greater than the '+' contrast value may be identified as the sensing value singularity region.

According to the fourth sensing value singularity discrimination embodiment of the present invention, the data reading unit 735 reads out the recognition data in the region of the ridge region included in the designated region of the recognition data, And an area including an area that is equal to or less than a set '-' contrast value can be identified as the sensing value singularity area.

According to the fifth sensing value singularity discrimination embodiment of the present invention, the data reading unit 735 reads out the area of the ridge area included in the designated area of the recognition data, which is greater than or equal to a predetermined '+' A region in which regions having a contrast value equal to or less than a set '-' contrast value are connected to each other can be identified as the sensing value singularity region.

According to the sixth sensing value singularity discrimination embodiment of the present invention, the data reading unit 735 reads the first sensing value singularity singularity of the first to fifth sensing singularity singularities, It is possible to identify the sensing value singularity area, and thus the present invention is not limited thereto.

According to the method of the present invention, the data reading unit 735 determines the base sensing value of the ridge area included in each recognition data through at least one of the first to fourth base sensing value determination examples Identifying at least one of S sensing value singularity regions distributed on the ridge region using the determined base sensing value through at least one of the first through sixth sensing value singularity identifying embodiments, The sensing value singularity area of at least s among the S sensed value singularity areas can be recognized as s pore positions.

When the recognition data capable of distinguishing the ridge region and the bony region are generated through the recognition data generation unit 730 and the ridge region and the pore position of each recognition data are recognized through the data reading unit 735, ) Associates the ridge region and the pore position of the m (1? M <M) recognition data and the (m + 1) recognition data among the generated M pieces of recognition data.

According to the embodiment of the present invention, even if the user tries to move his / her body part to the sensor part 705 and thus does not move during the sensing, the part of the body that touches the sensor part 705 can move only slightly And there is a need for correction by the above-described motion in generating cumulative difference data.

Accordingly, the matching processing unit 740 matches the ridge region and / or the pore position of the (m + 1) -th recognition data generated in units of the specified fine swallow identification period ms to the same ridge region and / or pore position of the m- Thereby correcting the separation due to the movement of the user's body part in contact with the sensor unit 705. [ Preferably, the matching processing unit 740 can correct not only the fine motion but also the large motion that does not deviate from the sensing range.

According to the first recognition data matching embodiment of the present invention, the matching processing unit 740 identifies a ridge pattern of the m-th recognition data and the (m + 1) -th recognition data, (M + 1) -th recognition data and the (m + 1) -th recognition data on the basis of the positions of the s pores existing in the matching ridge region and the geometric relationships they form, By matching the recognition data, the m-th recognition data and the (m + 1) -th recognition data can be precisely matched.

According to the second recognition data matching embodiment of the present invention, the matching processing unit 740 is configured to perform, in cooperation with the fingerprint recognition unit 715 (or in itself), a feature point on the ridge pattern corresponding to the m- (m + 1) -th recognition data, and the feature point on the ridge pattern corresponding to the m-th recognition data and the feature point on the ridge pattern corresponding to the (m + Matching the m-th recognition data with the (m + 1) -th recognition data on the basis of the identified ridge pattern, and calculating a geometric relationship between the positions of the s pores existing in the matching ridge region, The m-th recognition data and the (m + 1) -th recognition data can be matched precisely by matching the m-th recognition data with the (m + 1) -th recognition data.

According to the third recognition data matching embodiment of the present invention, the matching processing unit 740 may be configured to combine the m-th recognition data and the (m + 1) -th recognition data in such a manner as to at least partially combine the first to second recognition data matching embodiments ) Matching data, and thus the present invention is not limited thereto.

The difference data generator 745 subtracts the sensed value of the m-th recognition data from the sensed value of the (m + 1) -th recognition data matched by the matching processor 740 to generate the n-th data. Preferably, the difference data generator 745 repeats the process of subtracting the sensed value of the m-th recognition data from the sensed value of the (m + 1) -th recognition data to generate (M-1) .

According to the embodiment of the present invention, even when the user tries to apply the same (or even) contact pressure while sensing his / her body part to the sensor part 705 while sensing, the contact pressure applied to the sensor part 705 The change of the contact pressure is reflected in the change of the sensing value. Therefore, correction of this contact pressure change is necessary in generating the difference data.

Then, the difference data generator 745 calculates a correction coefficient for matching the base sensing value of the ridge area of the (m + 1) -th recognition data with the base sensing value of the ridge area of the m-th recognition data, The n-th order data can be generated by subtracting the sensing value of the m-th recognition data from the sensing value of the (m + 1) -th recognition data based on the correction coefficient. Here, the base sensing value may be defined as a sensing value corresponding to a ridge portion in contact with the sensor unit 705 of a user's body region, and / or a sensing value serving as a reference for distinguishing a ridge region and a bony region.

According to the first base sensing value determination method of the present invention, the difference data generation unit 745 calculates a region distinguishing value for distinguishing the ridge region and the bony region among the sensing values included in each recognition data, Can be determined as the base sensing value of the ridge region. On the other hand, according to the method, the difference data generation unit 745 substitutes the calculated area discrimination value into a specified correction operation formula (for example, an operation formula for correcting the error) to a base sensing value of the ridge area You can decide.

According to the second base sensing value determination method of the present invention, the difference data generation unit 745 calculates the sensing value distribution of the ridge area using the sensing value included in each recognition data, A value belonging to the designated distribution area can be determined as the base sensing value of the ridge area. For example, when the sensing value distribution of the designated area is in the form of a normal distribution, the difference data generator 745 may calculate a value belonging to the average (m) of the normal distribution or the standard deviation Can be determined as the base sensing value of the ridge region. Meanwhile, the difference data generator 745 may substitute a value belonging to the designated distribution area on the sensing value distribution into the specified correction equation according to the embodiment, and determine the calculated value as the base value of the ridge area.

According to the third base sensing value determination method of the present invention, the difference data generation unit 745 calculates an average value of the sensing values included in each recognition data, and outputs the calculated average value as the base sensing value of the ridge region You can decide. Meanwhile, the difference data generator 745 may determine the calculated value by substituting the calculated average value into the specified correction operation equation as the base sensing value of the ridge region.

According to the fourth base sensing value determination embodiment of the present invention, the difference data generation section 745 may modify at least one of the first to third base sensing value determination embodiments, or may partially combine two or more embodiments The base sensing value for each ridge region of each recognition data can be determined, and thus the present invention is not limited thereto.

The base sensing value for the ridge area of the (m + 1) -th recognition data and the base sensing value for the ridge area of the m-th recognition data may be obtained through at least one of the first to fourth base sensing values determination examples The difference data generator 745 calculates a correction coefficient for matching the base sensing value of the ridge area of the (m + 1) -th recognition data with the base sensing value of the ridge area of the m-th recognition data Then, the n-th order data may be generated by subtracting the sensing value of the m-th recognition data from the sensing value of the (m + 1) -th recognition data based on the correction coefficient. For example, when the sensing value of the m-th recognition data is subtracted from the sensing value of the (m + 1) -th recognition data based on the correction coefficient, the sensing value corresponding to the base sensing value mutually matched by the correction coefficient The value can be '0'.

During (or after) (M-1) pieces of difference data are generated through the difference data generation unit 745, the fine sweat discrimination unit 750 receives the difference data m (m) through the difference data generation unit 745 (M + 1) -th recognition data as shown in FIG. 8B by reading the difference value of the m-th data generated by subtracting the sensed value of the m-th recognition data from the sensed value of the And the difference value on the ridge area included in the m-th order data generated by subtracting the sensed value of the m-th recognition data such as the m-th difference data as shown in FIG. 8 . If at least one difference peculiar area as shown in FIG. 8C is identified on the ridge area included in the m-th order data, the fine sweat determining part 750 determines the differential peculiar area identified in the m-th data as a pore on the ridge area It is judged that the sweat is fine.

According to the technical features of the present invention, since fine sweat is sweated only in living body, it can be certified that the fine sweat is actually sweated in the living body just by discriminating the fine sweat. However, in order to block attempts to simulate the minute sweating of the fine sweat in the distant future, the present invention may be applied to the micro sweat discrimination unit 750 to determine whether there is a living sweat pattern as shown in Figs. 5A to 5C can do.

Referring to FIG. 7, the apparatus 700 may be configured such that, in the discrimination of at least one difference singular point area in the m-th order data through the fine sweat discriminating unit 750, (I + j) pieces of recognition data existing between the generated at least i (i? 1) pieces of recognition data and j (j? 0) pieces of recognition data generated after the mth piece of recognition data, (Mi) recognition data (or first recognition data) included in the (i + j) pieces of recognition data and a (n + 1) (n + 1) (mi? n <m + j) recognition data in which the ridge region and the bony region are matched with each other, (i + j-1) cumulative difference data obtained by subtracting (mi) recognition data (or first recognition data) (I + j-1) cumulative difference data and comparing the cumulative difference value corresponding to the difference-specific region with the cumulative difference value of the cumulative difference base value within a predetermined effective living body swallowing time And if the time required for the cumulative difference value of the difference point region to exceed the threshold value is within the effective living body swallowing time, the living body peculiar region is authenticated as the actual living body swallowing. And an authentication result processing unit 765 for processing the result of actual biometric authentication of the user's body part including the actual biometric authentication result.

When at least one difference-specific region is determined on the ridge area of the m-th order data through the fine sweat discriminating unit 750, the data reading unit 735 reads the m-th recognition data, (I + j) pieces of recognition data existing between j (j? 0) pieces of recognition data generated after the m-th recognition data and at least i (i? Data is read, and the ridge region and the bony region included in each of the (i + j) pieces of recognized recognition data are read. Preferably, the data reading unit 735 can read the ridge region and the bony region included in the recognition data using a pattern recognition algorithm.

When the ridge region and the bony region of each recognition data are read through the data reading unit 735, the matching processing unit 740 reads the (mi) recognition data (or (i + j) 1 recognition data) and the ridge region and the bony region of the (n + 1) -th recognition data.

According to the embodiment of the present invention, even if the user tries to move his / her body part to the sensor part 705 and thus does not move during the sensing, the part of the body that touches the sensor part 705 can move only slightly And there is a need for correction by the above-described motion in generating cumulative difference data.

The matching processing unit 740 then stores the ridge region and the bony region of the (n + 1) -th recognition data generated in units of the specified sensing period ms in the same ridge region of the (mi) recognition data (or the first recognition data) Region of the user's body that is in contact with the sensor unit 705 is corrected. Preferably, the matching processing unit 740 can correct not only the fine motion but also the large motion that does not deviate from the sensing range.

According to the first recognition data matching embodiment of the present invention, the matching processing unit 740 confirms the ridge pattern of the (mi) recognition data (or the first recognition data) and the (n + 1) (Mi) recognition data (or first recognition data) and (n + 1) recognition data based on the identified ridge pattern. For example, the matching processing unit 740 overlaps the ridge pattern of the ridge pattern of the (mi) recognition data (or the first recognition data) and the ridge pattern of the (n + 1) ) Recognition data (or first recognition data) and the (n + 1) recognition data.

According to the second recognition data matching embodiment of the present invention, the matching processing unit 740 checks the bone patterns of the (mi) recognition data (or first recognition data) and the (n + 1) (Mi) recognition data (or first recognition data) and (n + 1) recognition data on the basis of the identified bone pattern. For example, the matching processing unit 740 overlaps the same bone pattern among the bone pattern of the (mi) recognition data (or the first recognition data) and the bone pattern of the (n + 1) ) Recognition data (or first recognition data) and the (n + 1) recognition data.

According to the third recognition data matching embodiment of the present invention, the matching processing unit 740 may be configured to match the (mi) recognition data (or the first recognition data) in cooperation with the fingerprint recognition unit 715 Characterized in that the minutiae point on the corresponding ridge pattern and the minutiae point on the ridge pattern corresponding to the (n + 1) -th recognition data are identified and the minutiae points on the ridge pattern corresponding to the (mi) recognition data (or first recognition data) (Mi) recognition data (or first recognition data) and (n + 1) recognition data (or second recognition data) on the basis of the minutiae by matching the same minutiae among minutiae on the ridge pattern corresponding to the Can be matched.

According to the fourth recognition data matching embodiment of the present invention, the matching processing unit 740 may be configured to combine at least two of the first through third recognition data matching embodiments with the (mi) recognition data 1 recognition data) and the (n + 1) recognition data, so that the present invention is not limited thereto.

The accumulated difference data generation unit 755 calculates the sensing value of the (mi) recognition data (or the first recognition data) from the sensed value of the (n + 1) recognition data matched by the matching processing unit 740 And generates n-th accumulated difference data. Preferably, the cumulative difference data generator 755 repeats the process of subtracting the sensed value of the (mi) recognition data (or the first recognition data) from the sensed value of the (n + 1) i + j-1) cumulative difference data.

According to the embodiment of the present invention, even when the user tries to apply the same (or even) contact pressure while sensing his / her body part to the sensor part 705 while sensing, the contact pressure applied to the sensor part 705 The change of the contact pressure is reflected in the change of the sensing value. Therefore, correction of this contact pressure change is necessary in generating cumulative difference data.

The accumulated difference data generator 755 generates the accumulated difference data 755 based on the base sensing value of the ridge area of the (n + 1) -th recognition data and the base sensing value of the ridge area of the (mi) recognition data (Mi) recognition data (or first recognition data) is subtracted from the sensing value of the (n + 1) -th recognition data based on the correction coefficient, and the n-th accumulation The difference data can be generated. Here, the base sensing value may be defined as a sensing value corresponding to a ridge portion in contact with the sensor unit 705 of a user's body region, and / or a sensing value serving as a reference for distinguishing a ridge region and a bony region.

According to the first base sensing value determination method of the present invention, the accumulated difference data generation unit 755 calculates a region distinguishing value for distinguishing the ridge region and the bony region among the sensing values included in each recognition data, And the calculated area discrimination value can be determined as the base sensing value of the ridge area. On the other hand, according to the embodiment, the accumulated difference data generation unit 755 substitutes the calculated area discrimination value into a specified correction expression (for example, an expression for correcting the error) and outputs the calculated value to the base sensing value .

According to the second base sensing value determination method of the present invention, the cumulative difference data generation unit 755 calculates the sensing value distribution of the ridge region using the sensing value included in each recognition data, A value belonging to the designated distribution region on the ridge region can be determined as the basis sensing value of the ridge region. For example, when the sensing value distribution of the designated area is in the form of a normal distribution, the cumulative difference data generator 755 may calculate the cumulative difference data by multiplying the average (m) of the normal distribution or a value belonging to the standard deviation Can be determined as the base sensing value of the ridge region. Meanwhile, according to the embodiment, the accumulated difference data generator 755 may substitute a value belonging to the designated distribution area on the sensing value distribution into the specified correction equation, and determine the calculated value as the base value of the ridge area.

According to the third base sensing value determination method of the present invention, the accumulated difference data generation unit 755 calculates an average value of the sensing values included in each recognition data, and outputs the calculated average value to the base sensing value . Meanwhile, the cumulative difference data generator 755 may calculate the calculated value by substituting the calculated average value into the specified correction equation according to the method, and determine the calculated value as the base sensing value of the ridge area.

According to the fourth base sensing value determination embodiment of the present invention, the cumulative difference data generation unit 755 may modify at least one of the first to third base sensing value determination embodiments, It is possible to determine the base sensing value for the ridge region of each recognition data by combining them, and thus the present invention is not limited thereto.

(Mi) recognition data (or first recognition data) for the ridge region of the (n + 1) -th recognition data through at least one of the first to fourth base sensing value determination embodiments, When the base sensing value for the ridge area of the (n + 1) th recognition data is determined, the cumulative difference data generator 755 generates the cumulative difference data for the ridge area of the (n + 1) (Mi) recognition data (or first recognition) data from sensing values of the (n + 1) -th recognition data on the basis of the correction coefficient after calculating a correction coefficient for matching the base- The nth cumulative difference data can be generated by subtracting the sensed value of the nth cumulative difference data. For example, when the sensing value of the (mi) recognition data (or the first recognition data) is subtracted from the sensing value of the (n + 1) recognition data based on the correction coefficient, The sensing value corresponding to the matched base sensing value may be '0'.

(N + 1) -th recognition data by subtracting the sensing value of the (mi) recognition data (or the first recognition data) from the sensed value of the (n + 1) -th recognition data through the accumulation difference data generation unit 755 The biometric data generation unit 760 compares the generated (i + j-1) pieces of accumulated difference data during (or after) (i + j-1) The time taken for the cumulative difference value of the differential region of the difference discriminated by the fine sweat discrimination unit 750 to occur from the specified cumulative difference base value to be equal to or higher than the specified threshold value is within a predetermined effective living sweating time as shown in FIG. Calculate analytically. Preferably, the threshold value may be set to a value proportional to the sensitivity of the sensor unit 705.

According to the method of the present invention, when verifying the bio-sweating of fine sweat, the effective bio-sweating time can be set to be less than 1 second, and in the case of verifying bio-sweating including both fine sweat and wet sweat, The sweating time can be set within 2 seconds (maximum 5 seconds or less). According to the method of the present invention, when the effective living body sweating time is set to a short time unit (for example, less than 1 second), the probability of successful authentication by detecting wet sweat body sweating is reduced, but the probability of detecting fine sweat is at least Up to a 100ms time unit can be maintained above a certain level. On the other hand, when the effective living body sweating time is set to a relatively long period (for example, 2 seconds or more), the probability of successful authentication is increased by detecting not only fine sweat but also sweat of living body sweat. On the other hand, the effective living body sweating time may vary from person to person or from race to person, and may vary depending on environmental factors including season, temperature, and humidity. The living body swallow authentication unit 760 can set an effective living body swallowing time corresponding to the user by repeating the living body swallowing authentication of a legitimate user more than a designated number of times and can perform seasonal confirmation or various sensors The effective living body sweating time can be adjusted by reflecting an environmental factor such as temperature or humidity through the server (or the server).

If the time required for the cumulative difference value of the difference region to be abnormal is within the effective living body sweating time, the living body sweating authentication unit 760 generates the discriminated differential region of the living body from the living body And generates a micro vivo authentication result. On the other hand, if the difference peculiar area is not discriminated or is discriminated, if the time required for the cumulative difference value of the difference peculiar area to exceed the threshold value exceeds the effective living body sweating time, the living body swallow authentication unit 760 Biometric authentication result corresponding to the authentication failure can be generated.

The authentication result processing unit 765 may generate a final authentication result on the user's actual living body sweating using the living body living body authentication result generated through the living body living body authentication unit 760. Alternatively, the authentication result processing unit 765 may combine the fingerprint matching result generated through the fingerprint matching unit 725 and the micro vivo authentication result generated through the biometric swallowing authentication unit 760, In this case, the authentication result processing unit 765 determines whether the ridge pattern of the fingerprint matches through the fingerprint matching unit 725, The body part of the user sensed through the sensor unit 705 can be generated in the final authentication result when the biometric body authentication is successfully performed through the authentication unit 760. If not, The authentication result can be generated. The authentication result processing unit 765 may output the generated final authentication result through the designated means or provide the designated final authentication result through the designated path.

According to the method of the present invention, the fingerprint matching result is used as an identification means for identifying the user, and the micro-biometric authentication result can be used as an authentication means for authenticating that the identified user is actually the user.

8A to 8C are diagrams illustrating the relationship between the recognition data and the differential point region according to the method of the present invention.

FIG. 8B illustrates a sensing value on the ridge area of the m-th recognition data among the M recognition data generated in the unit of the specified microevolution identification period ms, and FIG. 8B is a view showing a ridge of the (m + 1) 8C shows the sensing value of the m-th data obtained by subtracting the sensing value of the m-th recognition data illustrated in FIG. 8C from the sensing value of the (m + 1) -th recognition data illustrated in FIG. 8B The difference value is exemplified.

The example of FIG. 8A illustrates a sensing value of the state before fine sweat is generated in the pore on the ridge area. The example of FIG. 8B illustrates the sensing value when fine sweat is generated in the pore on the same ridge area. Therefore, if the sensing value of FIG. 8A is subtracted from the sensing value of FIG. 8B, difference data including the difference value as shown in FIG. 8C can be generated.

9 is a diagram showing the relationship between the sensing period and the effective living body swallowing time according to the method of the present invention.

The living body sweating by the human body has the sweating pattern as shown in Figs. 5A to 5C. The present invention sets an effective living body sweating time for authenticating living body sweating, and a sensing period for N-times effective sensing by touching one sensor unit 705 is shorter than an effective living body sweating time set for actual living body authentication Time period.

On the other hand, the minute sweating identification period may be the same or different from the effective sweating time. For the sake of convenience, FIG. 9 illustrates a case where the minute sweating identification period and the effective living body sweating time are set to be the same. In the case of authenticating the sweating of the wet sweat, the effective sweating time is preferably set to be larger than the minute sweating identification period, and when the sweating of the micro sweat is authenticated, the effective sweating time is the same or similar range Lt; / RTI &gt; For example, the fine swallow identification period may be set in units of 100 ms.

FIG. 10 is a diagram illustrating a process of authenticating an actual living body sweating according to an embodiment of the present invention.

In more detail, FIG. 10 illustrates an example of a method of detecting N (N &gt; = 3) times of a body part of a user including a fingerprint using a sensing unit 705 capable of sensing a fingerprint in units of a predetermined sensing period ms (M + 1) (1? M <M) recognition data by matching the fingerprint ridge area and the porthole area of the M (M? 3) (M-1) pieces of difference data by subtracting the m-th recognition data to discriminate at least one differential sphere region having a predetermined threshold value or more from the specified difference base value, (I + j) pieces of recognition data existing between at least i (i? 1) pieces of recognition data and j (j? 0) pieces of recognition data generated after the mth recognition data, (N + 1) &lt; / RTI &gt; (m (i + j-1) cumulative difference data obtained by subtracting the (mi) recognition data (or first recognition data) from the recognition data, 1) cumulative difference data, and when the cumulative difference value corresponding to the difference point region is within a predetermined effective living body sweating time longer than a specified threshold value in the specified cumulative difference base value, 10 is a flowchart illustrating a method for authenticating a living body sweating according to an embodiment of the present invention. Referring to FIG. 10, It is to be understood that the invention may be practiced otherwise than as specifically described herein, but it will be appreciated that the invention may be practiced otherwise than as specifically described herein, The technical features are not limited by the method alone.

Referring to FIG. 10, an apparatus 700 of the present invention acquires N sensed data obtained by sensing a user's specific body part N times in a specified ms unit through a sensor of a sensor unit 705 (1000). When at least one sensing data is obtained, the device 700 processes and reads the sensed data according to a specified fingerprint recognition algorithm to extract feature points of the ridge pattern (1005). If the user's fingerprint is registered, the device 700 generates a fingerprint template corresponding to the extracted feature point and stores the generated fingerprint template in a designated storage area (1010). On the other hand, if the fingerprint of the user is to be authenticated, the device 700 compares the extracted fingerprint template stored in the designated storage area with the extracted minutiae to check whether the fingerprint is matched (1015), and generates a fingerprint matching result corresponding to the result do.

The biometric authentication process of the present invention may be performed sequentially with the fingerprint recognition and authentication process according to a specified sequence or may be performed by multiplexing with the fingerprint recognition and authentication process. During or after acquiring the N sensed data, the device 700 reads the sensed value of each sensed data obtained to distinguish ridges and valleys from each other, (1025). &Lt; / RTI &gt; Preferably, the apparatus 700 may generate recognition data (1025) according to at least one of the first through fifth recognition data generation embodiments of the present invention.

The apparatus 700 recognizes 1030 the ridge area and the pore position of each of the M pieces of recognition data generated with the specified minute sweeping discrimination period among the generated recognition data and compares the m recognition data and the m +1) matching area between the ridge area of the recognition data and the pore position (1035). According to at least one embodiment of the first to third recognition data matching embodiments of the present invention, the apparatus 700 preferably includes the m-th recognition data and the (m + 1) -th recognition data based on the ridge region and the pore location, It is possible to match the recognition data.

The apparatus 700 includes (M-1) pieces of data including m-th subtraction data generated by subtracting the sensed value of the m-th recognition data from the sensed value of the matched (m + 1) At step 1040, the generated difference data is read to determine at least one differential region having a difference greater than a predetermined threshold in a specified difference base value in a ridge area (1045). If at least one differential region is not identified in the (M-1) pieces of differential data, the apparatus 700 repeats the process of discriminating the differential region by at least one time , Or generate a vital signs authentication result including a vital authentication failure (1075).

Meanwhile, when the m-th order data including at least one differential position area among the (M-1) pieces of differential data is identified, the apparatus 700 determines whether the differential position area is at least (i + j) pieces of recognition data existing between i (i? 1) pieces of recognition data and j (j? 0) pieces of recognition data generated after the mth recognition data, (1050), and the ridge region and the bony region of the (mi) recognition data (or the first recognition data) and the (n + 1) recognition data among the recognition data are matched (1055). Preferably, the apparatus 700 is further adapted to determine, based on at least one of the first through fourth recognition data matching embodiments of the present invention, Data) and the (n + 1) -th recognition data.

The apparatus 700 generates (i + j-1) cumulative difference data obtained by subtracting (mi) recognition data (or first recognition data) from the matched (n + 1) ). According to another embodiment of the present invention, the apparatus 700 generates (n + 1) pieces of difference data by matching the ridge region and the bony region of the generated N pieces of recognition data while generating the (M-1) (N-1) cumulative difference data obtained by subtracting the sensed value of the first recognition data from the sensed value of the recognition data (1? N <N). In this case, It is possible to confirm (i + j-1) cumulative difference data among the generated (N-1) cumulative difference data without generating data.

The apparatus 700 compares the (i + j-1) cumulative difference data to determine whether the cumulative difference value corresponding to the differential value area is greater than or equal to a predetermined effective value (1065). If the cumulative difference value of the difference point region exceeds the threshold value of the cumulative difference base value over a predetermined effective living body swallowing time, the device 700 may perform a living body swallowing authentication including a living body swallowing authentication failure The result is generated (1075).

On the other hand, if the cumulative difference value of the difference point region is within a predetermined effective living body sweating time, the device 700 authenticates the difference peculiar region as the actual living body sweating And generates a biometric authentication result (1070).

When the biometric authentication result is generated through the above process, the device 700 generates a final authentication result including the biometric authentication result, or combines the biometric authentication result with the fingerprint matching result, Generates a result (1080), and outputs the generated final authentication result or provides it to a designated path (1085).

700: Apparatus 705: Sensor section
710: sensing processing unit 715: fingerprint recognition unit
720: fingerprint storage unit 725: fingerprint registration unit
730: recognition data generation unit 735:
740: matching processing unit 745:
750: fine sweat discrimination unit 755: cumulative difference data generation unit
760: vital signs authentication unit 765: authentication result processing unit

Claims (30)

A method for performing a method through an apparatus having a sensor unit capable of sensing a fingerprint,
A sensing unit for sensing the user's body part through the sensor unit N (N &gt; = 3) times in a predetermined number of milliseconds to generate N pieces of recognition data capable of discriminating ridges and valleys of the fingerprint Stage 1;
Recognizing a ridge area and a pore position included in M (M &gt; = 3) recognition data generated in the unit of ms, (m + 1) -th recognition data by subtracting the sensed value of the m-th recognition data from the sensed value of the (m + 1) -th recognition data by associating the ridge region of the recognition data with the porthole position of the (M-1) pieces of difference data;
A third step of reading out the generated difference data to discriminate at least one differential sphere region in which a difference value on the ridge region is greater than or equal to a specified threshold value in a specified difference base value;
(I &gt; = 1) pieces of recognition data generated prior to the m-th piece of recognition data for which the difference-specific region is discriminated and at least one piece of j (n + 1) (mi? n <m + j) recognition data by matching the ridge region and the bony region included in each (i + j) A fourth step of generating (i + j-1) cumulative difference data by subtracting (mi) recognition data (or first recognition data);
(I + j-1) cumulative difference data to determine whether the cumulative difference value corresponding to the differential value region is greater than or equal to a predetermined threshold value in the cumulative difference base value within a predetermined effective living body swallowing time A fifth step of numerically calculating cognitive signals; And
And a sixth step of authenticating the discriminated differential region as a true micro-organism if the time required for the cumulative difference value of the difference region to be abnormal is within the effective living sweating time Authentication method.
2. The method of claim 1,
And a time period shorter than the set validated living body swallowing time.
3. The method according to claim 1 or 2, wherein the fine-
Wherein the sensing period is at least twice the sensing period.
The method according to claim 1,
Confirming a contact state of a user's body part through the sensor unit; And
And initiating effective sensing of the user's body part in a predetermined sensing period ms from a predetermined time in the contact state of the body part.
The method according to claim 1,
Sensing a user's body part in contact with the sensor unit in a specified sensing period ms; And
And starting to acquire data sensed from a certain time after the sensing of the sensed data as effective sensing data.
The method according to claim 1,
Confirming the release of contact of the body part during effective sensing of the user's body part in contact with the sensor part in a specified sensing period ms;
Determining whether a minimum sensing time set for authenticating the living body sweating has elapsed from the time when effective sensing of the body part is started upon confirming the disengagement of the body part;
And if the minimum sensing time has not elapsed, terminating the biometric biometric authentication process.
The method according to claim 1,
Confirming a sensing time that has elapsed since a valid sensing start time of a user's body part in contact with the sensor unit in a specified sensing period ms;
Confirming whether the confirmed sensing time exceeds a maximum sensing time set for authenticating a living body sweating;
And performing a procedure of suspending or resuming effective sensing of the body part when the maximum sensing time is exceeded.
2. The method according to claim 1,
And generating recognition data in which a sensing value corresponding to the ridge area and a sensing value corresponding to the bony area are distinguishably corrected so as to be distinguishable from each other.
2. The method according to claim 1,
The step of correcting the sensing value corresponding to the ridge area and the sensing value corresponding to the ridge area so as to be distinguishable and generating the recognition data corrected so as not to damage the sensing value corresponding to the pore disposed on the ridge area Wherein the microbial biomolecule authentication method comprises:
2. The method according to claim 1,
And generating recognition data in which at least one of a contrast, a contrast, and a saturation of the sensed values is corrected so that the ridge region and the bony region can be distinguished from each other.
2. The method according to claim 1,
Generating recognition data by correcting at least one noise among noise due to a sensor provided in the sensor unit, noise in a sensing process through the sensor unit, and noise due to contamination of a body part in contact with the sensor unit, Wherein the microbial biomolecule authentication method comprises:
2. The method according to claim 1,
Confirming a ridge pattern of the m-th recognition data and the (m + 1) -th recognition data; And
And matching the m-th recognition data with the (m + 1) -th recognition data based on the ridge pattern.
2. The method according to claim 1,
Confirming a bone pattern of the m-th recognition data and the (m + 1) -th recognition data; And
And matching the m-th recognition data with the (m + 1) -th recognition data based on the bone pattern.
2. The method according to claim 1,
Identifying minutiae points on the ridge pattern of the m-th recognition data and the (m + 1) -th recognition data; And
And matching the m-th recognition data with the (m + 1) -th recognition data based on the minutiae on the ridge pattern.
2. The method according to claim 1,
Determining a base sensing value of a ridge area included in the generated recognition data;
Identifying S (S? 3) sensing value singularity regions distributed on the ridge region using the basis sensing value; And
And recognizing at least s (3? S? S) sensing value singular points of the identified S sensed value singularity regions as s pore positions.
2. The method according to claim 1,
Confirming a geometric relationship formed by s (s? 3) pores existing in a ridge region of each recognition data; And
And matching the positions of the pores of the m-th recognition data and the (m + 1) -th recognition data using the geometric relationship of the pores.
2. The method according to claim 1,
Identifying minutiae points on the ridge pattern of the m-th recognition data and the (m + 1) -th recognition data; And
Confirming a geometric relationship formed by s (s? 3) pores existing in a ridge region of each recognition data based on the minutiae on the ridge pattern; And
And matching the positions of the pores of the m-th recognition data and the (m + 1) -th recognition data based on the geometric relationship of the pores.
The method according to claim 1,
Further comprising the step of calculating a correction coefficient for matching the base sensing value of the ridge area of the (m + 1) -th recognition data with the base sensing value of the ridge area of the m-th recognition data,
The second step may include generating m-th order data by subtracting the sensing value of the m-th recognition data from the sensing value of the (m + 1) -th recognition data based on the correction coefficient Actual microscopic biopsy authentication method.
The method according to claim 1,
Calculating an average of difference values of the generated difference data with respect to a ridge area and determining the difference value as a difference base value with respect to the ridge area.
The method according to claim 1,
Calculating a difference value distribution for the ridge area of the generated difference data and determining a value belonging to the specified distribution area on the difference value distribution as a difference base value of the ridge area; Microbial body authentication method.
2. The method of claim 1,
And a '+' threshold value or a '-' threshold value.
The method according to claim 1,
Wherein the biofilm is generated in the form of '+' peaks or '-' peaks.
The method as claimed in claim 1,
Confirming a ridge pattern of the (mi) recognition data (or first recognition data) and the (n + 1) recognition data; And
And matching the (mi) recognition data (or the first recognition data) with the (n + 1) recognition data based on the ridge pattern.
The method as claimed in claim 1,
Confirming a bone pattern of the (mi) recognition data (or first recognition data) and the (n + 1) recognition data; And
And matching the (mi) recognition data (or the first recognition data) with the (n + 1) recognition data based on the bone pattern.
The method as claimed in claim 1,
Identifying minutiae on the ridge pattern of the (mi) recognition data (or first recognition data) and the (n + 1) recognition data; And
And matching the (mi) recognition data (or the first recognition data) with the (n + 1) recognition data based on the minutiae points on the ridge pattern. .
The method according to claim 1,
And calculating a correction coefficient for matching the base sensing value of the ridge area of the (n + 1) -th recognition data with the base sensing value of the ridge area of the (mi) recognition data (or first recognition data) In addition,
The fourth step may include generating the n-th accumulated difference data by subtracting the sensing value of the (mi) recognition data (or the first recognition data) from the sensing value of the (n + 1) Wherein the step of authenticating the micro-organism comprises the step of authenticating the micro-organism.
The method according to claim 1,
Calculating an average of cumulative difference values of the generated cumulative difference data with respect to the ridge region and determining the cumulative difference base value for the ridge region as the cumulative difference value.
The method according to claim 1,
Calculating a cumulative difference value distribution for the ridge area of the generated cumulative difference data and determining a value belonging to the specified distribution area on the cumulative difference value distribution as the cumulative difference value of the ridge area; Authentic microbial biometric authentication method.
The method according to claim 1,
If at least one differential region is not determined using N pieces of recognition data,
(N + L) pieces of recognition data generated by L (L &gt; = 1) times more effective sensing in a predetermined sensing period ms to discriminate a difference point region.
The method according to claim 1,
Wherein the ridge region is used as an identification means for identifying the user,
Wherein the effective living body swallowing is used as an authentication means for authenticating that the user is an actual user.

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