KR20170061388A - Method for Certificating Living Body Sweating - Google Patents

Abstract

The present invention relates to a method for authenticating a living body sweating, and a method for authenticating a living body sweating according to the present invention, which is executed through an apparatus having a sensor unit capable of sensing a fingerprint, (N > = 2) sensed data obtained in units of milliseconds (N < 2 >) and sensing sensed values of the sensed data to obtain N (N + 1) (1 ≤ 1) of the recognition data, (n + 1) -th recognition data obtained by matching the positions of the portholes with the sensed values of the first recognition data, Accumu (N-1) cumulative difference recognition data including at least one of the (N-1) cumulative difference recognition data and the (N-1) And a sixth step of identifying at least one cumulative difference recognition singularity area in which a value equal to or larger than a predetermined threshold value has been generated, and authenticating the actual body sweating of the body part.

Description

[0001] The present invention relates to a method for certifying Living Body Sweating,

The present invention relates to a method and apparatus for detecting a fingerprint of a body portion of a user including fingerprints by N (N > = 2) valid sensing units in ms (millisecond) (N-1) pieces of data including n-th accumulated difference recognition data generated by subtracting the sensing value of the first recognition data from the sensing value of the (n + 1) The cumulative difference recognizing singularity area generated on the cumulative difference recognition data is discriminated and authenticated by the actual living body sweating of the body part.

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.

An object of the present invention to solve the above problem is to acquire N (N > = 2) sensed data obtained by sensing a user's body part in a specified millisecond unit through a sensor unit to discriminate ridges and valleys (N + 1) (1 < n < N) recognition data among the recognition data and recognizes a position of a pore existing in a ridge area of each recognition data generated, (N-1) cumulative accumulation recognition data generated by subtracting the sensed value of the first recognition data from the sensed value of the (n + 1) (N-1) pieces of cumulative difference recognition data, and generating at least one cumulative difference recognizing data in which a value equal to or larger than a predetermined threshold value is generated on a losing area of at least one cumulative difference recognizing data among the To determine that the area has a biometric authentication method that authenticates a perspiration actual living body perspiration of the part of the body to provide.

A method for authenticating a living body balancing method according to the present invention is a method executed by a device including a sensor unit capable of sensing a fingerprint, wherein N (N > A second step of acquiring 2 sensing data of the sensed data and reading out the sensed value of each sensed data to generate N recognition data distinguishable from a ridge and a valley; A third step of recognizing a position of a pore present in a ridge area of each recognition data to be generated; a third step of matching the first recognition data of the recognition data and the pore positions of the (n + 1) (N + 1) -th recognition data obtained by subtracting the sensed value of the first recognition data from the sensed value of the (n + 1) -th recognition data obtained by matching the position of the porthole N-1) dogs (N-1) pieces of cumulative difference recognition data; a fifth step of generating cumulative difference recognition data; at least one cumulative difference recognizing singularity And a sixth step of authenticating the real body sweating of the body part by discriminating an area.

According to another aspect of the present invention, there is provided a method of authenticating a living body, comprising the steps of: checking a contact state of a user's body part through the sensor unit; And starting to acquire the data.

According to another aspect of the present invention, there is provided a method for authenticating a living body part, comprising the steps of: acquiring sensing data obtained by sensing a user's body part in contact with the sensor unit in units of ms; And acquiring the sensed data as effective sensing data.

According to the present invention, the living body sweating authentication method further comprises the steps of: 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 units of ms; The method comprising the steps of: determining whether a minimum sensing time set for authenticating the body fluids has elapsed since the start of effective sensing of a body part; and performing a procedure for ending the body fluids authentication procedure when the minimum sensing time has not elapsed .

According to the present invention, the living body swallow authentication method further includes the steps of: confirming a sensing time that has elapsed from a point in time at which valid sensing of the user's body part in contact with the sensor unit is started in units of ms; Checking whether the maximum sensing time set for authentication is exceeded, and performing a procedure for stopping effective sensing of the body part when the maximum sensing time is exceeded.

According to the present invention, the second step may include generating recognition data in which a sensing value corresponding to the ridge region and a sensing value corresponding to the bony region are distinguishably corrected. Meanwhile, in the second step, the sensing value corresponding to the ridge region and the sensing value corresponding to the ridge region are discriminately corrected, and the sensed value corresponding to the pore disposed on the ridge region is corrected so as not to be damaged And generating the data. Meanwhile, the second step may include generating recognition data in which at least one of the contrast, the contrast, and the saturation of the sensing values is corrected so that the ridge region and the bony region can be distinguished from each other. Meanwhile, in the second step, recognition data obtained by correcting at least one noise among noise due to a sensor provided in the sensor unit, noise during a sensing process through the sensor unit, and noise due to contamination of a body part in contact with the sensor unit, And a step of generating the data.

According to another aspect of the present invention, there is provided a method of detecting a crosstalk according to the present invention, comprising the steps of: determining a base sensing value of a ridge area included in each generated recognition data; (3? S? S) of the sensed value singularity regions of the identified S sensed value singularity regions as s positions of pore holes, Step < / RTI > Meanwhile, the step 3a may include a step of calculating an area discrimination value distinguishing the ridge area and the bony area and determining the discrimination value as the ridge area sensing value. Meanwhile, the step 3a may include calculating a region distinguishing value for distinguishing the ridge region and the bony region, and correcting the region discrimination value to determine a base sensing value of the ridge region. Meanwhile, the step 3a may include calculating a sensing value distribution of the ridge region and determining a value belonging to the designated distribution region on the sensing value distribution as the ridge region sensing value. The step a) may include calculating a sensing value distribution of the ridge region, and correcting a value belonging to the designated distribution region on the sensing value distribution to determine a base sensing value of the ridge region. Meanwhile, the step 3a may include calculating a sensing value average of the ridge area and determining the sensed value as the ridge area sensing value. The step a) may include calculating a sensing value average of the ridge area and correcting the sensed value average to determine a base sensing value of the ridge area. Meanwhile, the step (3b) may include identifying a region on the ridge region that is equal to or greater than a predetermined '+' contrast value than the base sensing value as a sensing value singularity region. Meanwhile, the step 3b may include the step of identifying an area of the ridge area that is less than a predetermined '-' contrast value than the base sensing value as a sensing value singularity area. Meanwhile, the step 3b includes the step of identifying a region including a region which is equal to or greater than a preset '+' contrast value in an area of the ridge region that is less than a predetermined '-' contrast value than the base sensing value as a sensing value singularity region . Meanwhile, the step 3b includes the step of identifying an area including a region which is equal to or less than a preset '-' contrast value in an area of the ridge area which is greater than or equal to a predetermined '+' contrast value than the base sensing value as a sensing value singularity area . Meanwhile, the step 3b includes a step of identifying, as a sensing value singularity area, an area in which the area of the ridge area is greater than or equal to a predefined '+' contrast value and the predetermined '-' can do.

According to the present invention, the fourth step may include the steps of: confirming a geometric relationship formed by s (s? 3) pores existing in the ridge region of each recognition data; determining, based on the geometric relationship of the pores, (N + 1) -th recognition data with the position of the pore of the (n + 1) -th recognition data.

According to the present invention, the fourth step includes the steps of: identifying the minutiae points on the ridge pattern of the first recognition data and the (n + 1) th recognition data; comparing the minutiae points on the ridge pattern with the ridge area of each recognition data Identifying a geometric relationship formed by the existing s (s? 3) pores; and matching the positions of the pores of the (n + 1) -th recognition data with the first recognition data based on the geometric relationship of the pores .

According to the present invention, the living body swallow authentication method includes calculating a correction coefficient for matching a base sensing value for a ridge region of the (n + 1) recognition data with a base sensing value for a ridge region of the first recognition data (N + 1) -th recognition data by subtracting the sensed value of the first recognition data from the sensed value of the (n + 1) -th recognition data based on the correction coefficient, can do.

According to another aspect of the present invention, the living body swallow authentication method further includes calculating an average of cumulative difference recognition values for the ridge region of the generated cumulative difference recognition data to determine an accumulated difference recognition base value of the ridge region, The sixth step may include determining at least one cumulative difference recognition singularity area having a value equal to or greater than a predetermined threshold value based on the cumulative difference recognition base value and authenticating the actual body swallowing of the body part.

According to the present invention, the living body swallow authentication method further comprises a step of calculating an accumulated difference recognition value distribution for the ridge area of the generated cumulative difference recognition data, and a value belonging to the specified distribution area on the cumulative difference recognition value distribution, Recognizing at least one cumulative difference recognizing singularity area having a value greater than or equal to a specified threshold based on the cumulative difference recognition base value, And authenticating with actual biofeedback.

According to the present invention, the ridge area is used as identification means for identifying the user, and the swallow authentication 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 the fingerprints are copied, there is an advantage in that all types of fingerprints are simulated and the threat of hacking by forgery and falsification is originally blocked.

According to the present invention, in order to authenticate a living body sweating, even when a body part in contact with a sensor part moves or a contact pressure changes while sensing a user's body part through a sensor part, the living body sweating is accurately authenticated, The threat of hacking by the original has the advantage of blocking.

According to the present invention, there is an advantage in that identification and authentication are simultaneously provided through a single fingerprint by using fingerprint matching as identification means for identifying a user and using biometric perspiration as an authentication means for authenticating fingerprint of an actual user.

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 living body sweating according to an embodiment of the present invention.
FIG. 8 is a diagram illustrating a process of authenticating a 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 persists through the pores. The size of the sweat glands is about 50 times smaller than the thickness of the hair (about 50 to 70 um).

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 " micro sweat ". 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 " fine sweating ". 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.

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, crater-like pores having a diameter of about 200 μm are irregularly arranged at an interval of about 830 μm 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 living body sweating according to an embodiment of the present invention.

More specifically, FIG. 7 illustrates an example in which N sensing areas generated by sensing a body part of a user including a fingerprint by N (N &gt; = 2) times in units of a specified ms (millisecond) using a sensor part 705 capable of sensing a fingerprint (N + 1) (1? N <N) recognition data by subtracting the sensed value of the first recognition data from the sensed value of the (n + 1) (N-1) pieces of cumulative difference recognition data that are generated on the (N-1) pieces of cumulative difference recognition data, and authenticates the actual body sweating of the body part. Those skilled in the art will appreciate that various implementations of the apparatus 700 (e.g., some of the components may be omitted, or fragmented, or combined) may be referred to and / Would be chuhal, the present invention is made, including any exemplary way in which the inference, to which the technical feature that is not limited to the exemplary method shown in the figure 7.

The device 700 of the present invention collectively refers to a configuration for authenticating living body sweating through a pore formed on a ridge of a fingerprint using a sensor unit 705 capable of sensing a fingerprint, Or a chip) type, a distributed system including various devices and servers on a network, or 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 acquires N (N? 2) sensed data obtained by sensing a specified specific body part of a user in units of a specified ms (millisecond) through the sensor unit 705 A fingerprint recognition unit 715 that includes a sensing processing unit 710 and extracts minutiae points on a ridge pattern corresponding to a user's fingerprint using the sensed data according to an embodiment of the present invention; And a fingerprint matching unit 725 for authenticating the extracted feature points using a fingerprint template of a user previously stored in a designated storage area. 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 (N &gt; = 2) sensing data obtained by sensing the user's body part in a specified ms unit through the sensor of the sensor unit 705. For example, the sensing processing unit 710 may acquire sensing data obtained by sensing a user's body part in units of 100 ms (= 0.1 second), and may acquire sensing data in units smaller than 100 ms (for example, 50 ms, 20 ms, Etc.) or sensing data obtained by sensing a user's body part in a unit larger than 100 ms (for example, 200 ms) can be obtained. That is, the period of sensing the body part of the user through the sensor of the sensor part 705 can be variously determined according to the performance of the device 700 (or the sensor part 705).

According to the embodiment of the present invention, the sensing processor 710 obtains N sensing data obtained by sensing the same body part N times in ms while the body part of the user is in contact with the sensor part 705. Preferably, the sensing processor 710 acquires image sensing data of pixels. It is preferable that one pixel of the sensing data is 50 mu 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 acquire N sensed data that have been effectively sensed in units of 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. Even so, someone in the future can try to simulate the fingerprints and pores of others and simulate vital sweats through the simulated pores using expensive control equipment.

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, It is possible to exclude the source from attempting to simulate the body sweating by starting to acquire the sensed data in which the user's body part is sensed effective in ms units from an unspecified arbitrary time while checking the contact of the body part.

According to the third 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 detects the user's body part contacting the sensor unit 705 in units of ms And sensing data sensed from an unspecified arbitrary time in the sensed sensing data is acquired as effective sensing data, so that even an attempt to simulate living body sweating can be excluded.

According to the fourth sensing method of the present invention, the sensing processing unit 710 may combine at least two of the first to third sensing methods to acquire N sensed data, have.

Meanwhile, the sensing processing unit 710 confirms whether or not the contact of the body part is released during effective sensing of the user's body part in contact with the sensor unit 705 in units of 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 valid sensing is started in ms units of the user's body part in contact with the sensor unit 705, and the confirmed sensing time may be used to authenticate the living body sweating (For example, 500 ms, 1 second, 2 seconds, etc.). If the effective sensing time exceeds the set maximum sensing time, the sensing processing unit 710 stops the sensing of the body part (for example, stops the sensing or ignores the sensing data) You can exclude more and more attempts to simulate. 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 position of a pore existing in a ridge area of each recognition data to be generated; and a data reading unit 735 for reading the first recognition data and the (n + 1) (N + 1) -th recognition data obtained by subtracting the sensed value of the first recognition data from the sensed value of the (n + 1) -th recognition data obtained by matching the position of the porthole, (N-1) cumulative difference recognition data generating units 745 for generating cumulative difference recognition data including (N-1) cumulative difference recognition data, The ridge region of the difference data And at least one cumulative difference recognizing singularity area having a value equal to or greater than a threshold value specified in the body part identification result, and authenticating the actual body part sweating of the body part, wherein the body part authentication part And an authentication result processing unit 755 for processing the authentication result.

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.

The data reading unit 735 reads out the recognition data generated by the recognition data generation unit 730 from the ridge region of each recognition data generated through the recognition data generation unit 730, And recognizes the position of the pores present in the pawl.

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 pore position of each recognition data is recognized through the data reading unit 735, The first recognition data among the N recognition data to be generated is matched with the pore position of the (n + 1) (1? N <N) 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 it is necessary to perform the correction by the above-described motion in generating the cumulative difference recognition data.

Accordingly, the matching processor 740 confirms the geometric relationship formed by the s pores existing in the ridge area of each recognition data generated in the unit of ms and calculates the geometric relationship between the first recognition data and the second recognition data based on the geometric relationship of the identified pores The first recognition data and the (n + 1) -th recognition data can be matched by matching the pore positions of the (n + 1) -th recognition data.

Meanwhile, the matching processing unit 740 performs a matching process on the minutiae points on the ridge pattern corresponding to the first recognition data and the ridge pattern corresponding to the (n + 1) -th recognition data in cooperation with the fingerprint recognition unit 715 (S &gt; = 3) paired pores that match each other on the ridge area of each recognition data on the basis of the minutiae points on the ridge pattern are confirmed, and based on the geometric relationship of the identified pores, , The first recognition data and the (n + 1) -th recognition data can be matched by matching the positions of the pores of the first recognition data and the (n + 1) -th recognition data.

The cumulative difference recognition data generation unit 745 subtracts the sensed value of the first recognition data from the sensed value of the (n + 1) recognition data in which the positions of the pores are matched by the matching processing unit 740, And generates cumulative difference recognition data. Preferably, the cumulative difference recognition data generator 745 repeats the process of subtracting the sensed value of the first recognition data from the sensed value of the (n + 1) And generates data.

According to the embodiment of the present invention, even when the user tries to contact the body part of his / her body with the sensor part 705 and try to apply the same (or even) contact pressure to the sensor part while sensing, 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 recognition data.

The cumulative difference recognition data generation unit 745 calculates a correction coefficient for matching the base sensing value of the ridge area of the (n + 1) recognition data with the base sensing value of the ridge area of the first recognition data (N + 1) -th recognition data by subtracting the sensed value of the first recognition data from the sensed value of the (n + 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. On the other hand, if the base sensing value of each recognition data is determined through the data reading unit 735, the base sensing value can be used without determining it separately.

According to the first base sensing value determination method of the present invention, the accumulated difference recognition 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, The calculated region discrimination value may be determined as the base sensing value of the ridge region. Meanwhile, the cumulative difference recognition data generation unit 745 substitutes the calculated area discrimination value into a specified correction formula (for example, an equation for correcting the error) according to the method, and outputs the calculated value to the base- Value. &Lt; / RTI &gt;

According to the second base sensing value determination method of the present invention, the cumulative difference recognition 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 on the distribution 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 cumulative difference recognition data generation unit 745 generates the cumulative difference recognition data 745 by multiplying the average (m) of the normal distribution or the standard deviation Value can be determined as the basis sensing value of the ridge area. Meanwhile, the cumulative difference recognition data generation unit 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 cumulative difference recognition data generation unit 745 calculates the average value of the sensing values included in each recognition data, and outputs the calculated average value to the base sensing Value. &Lt; / RTI &gt; Meanwhile, according to the embodiment, the cumulative difference recognition data generation unit 745 can substitute the calculated average value into the designated correction formula, and determine the calculated value as the base value of the ridge area.

According to the fourth base sensing value determination embodiment of the present invention, the cumulative difference recognition data generation section 745 may modify at least one of the first to third base sensing value determination embodiments, To determine a base sensing value for a ridge region of each recognition data, and thus the present invention is not limited thereto.

The base sensing value for the ridge region of the (n + 1) -th recognition data and the base sensing value for the ridge region of the first recognition data may be obtained through at least one of the first to fourth base sensing value determination embodiments If it is determined that the cumulative difference recognition data generation unit 745 generates 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 first recognition data, The nth cumulative difference recognition data may be generated by subtracting the sensed value of the first recognition data from the sensed value of the (n + 1) recognition data based on the correction coefficient. For example, when the sensing value of the first recognition data is subtracted from the sensing value of the (n + 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'.

(N-1) cumulative difference recognition data generated by subtracting the sensed value of the first recognition data from the sensed value of the (n + 1) recognition data through the cumulative difference recognition data generation unit 745, During the generation (or after the generation) of the difference recognition data, the biometric validity authentication unit 750 acquires a threshold value of a predetermined threshold value on the ridge area of at least one cumulative difference recognition data among the (N-1) Recognizes at least one cumulative difference recognition singularity area in which the above-mentioned value has occurred, and authenticates the actual body sweating of the body part. Preferably, the threshold value may be set to a value proportional to the sensitivity of the sensor unit 705.

According to an embodiment of the present invention, the threshold value may include a '+' threshold or a '-' threshold, and the cumulative difference recognition singularity may occur in a '+' , And the cumulative difference recognition singularity area may include an area having a diameter of 200 mu m or less. For example, if the sweat pores existing on the ridge area of the first recognition data are in an erratic state and the living body sweating occurs in the (n + 1) -th recognition data, the area corresponding to the sweat pores in which the living body sweating has occurred is' + &Quot; threshold value may occur.

According to the first swallowing authentication method of the present invention, the vital sign authentication unit 750 checks the cumulative difference recognition base value of the ridge area of the generated cumulative difference recognition data, and based on the cumulative difference recognition base value Recognizing at least one cumulative difference recognition singularity area in which a value equal to or larger than a specified threshold value has occurred, and authenticating the actual body sweating of the body part. For example, when no noise is generated during sensing through the sensor unit 705, the sensed value of the ridge area of the cumulative difference recognition data is flattened except for the cumulative difference recognizing singularity area, The controller 750 may check the cumulative difference recognition base value to a value flattened in the ridge area.

On the other hand, in the sensing process through the sensor unit 705, it is possible to acquire sensed data with high noise, and such noise also propagates to the cumulative difference recognition data. In this case, the body fatness authentication unit 750 may determine the cumulative difference recognition base value in consideration of the noise, and determine the cumulative difference recognition singularity region based on the determined cumulative difference recognition base value to authenticate the living body sweating.

According to the second sweating authentication method of the present invention, the biometric swath authenticating unit 750 calculates the cumulative difference recognition value value for the ridge area of the generated cumulative difference recognition data, Recognizing at least one cumulative difference recognition singularity area having a value greater than or equal to a specified threshold based on the cumulative difference recognition base value to authenticate the actual body swallowing of the body part. Meanwhile, according to an embodiment of the present invention, the body fatness authentication unit 750 can determine the cumulative difference recognition base value of the ridge region by correcting the average of the cumulative difference recognition values, At least one cumulative difference recognition singularity area in which the value is generated can be identified and authenticated by the actual living body sweating of the body part.

According to the third outpatient authentication method of the present invention, the vital sign authentication unit 750 calculates the cumulative difference recognition value distribution for the ridge area of the generated cumulative difference recognition data, Recognizing at least one cumulative difference recognizing singularity area having a value equal to or greater than a predetermined threshold value based on the cumulative difference recognition base value, It can be certified as vital sweating. Meanwhile, according to an embodiment of the present invention, the vital sign authentication unit 750 can determine a cumulative difference recognition base value of the ridge area by performing a correction calculation on a value belonging to a designated distribution area on the cumulative difference recognition value distribution, At least one cumulative difference recognition singularity region having a value equal to or greater than a predetermined threshold value is determined and authenticated by the actual living body sweating of the body part.

According to the fourth swallowing authentication example of the present invention, the living body swallow authentication unit 750 determines a cumulative difference recognition base value by partially combining two or more embodiments among the first to third swallowing authentication embodiments, Recognizing at least one cumulative difference recognition singularity area having a value greater than or equal to a specified threshold based on the cumulative difference recognition base value and authenticating the actual body sweating of the body part. For example, some regions of the cumulative difference recognition data ridge region may be highly noisy and other ridge regions may be less or flat. In this case, the vital sign authentication unit 750 divides the ridge area of the cumulative difference recognition data to determine the cumulative difference recognition base value for each area, and determines the cumulative difference recognition singularity area based on the determined ridge difference recognition area. have.

The living body swallow authentication unit 750 generates a living body swallow authentication result corresponding to the result of authenticating the living body swallow through at least one of the first to fourth swallowing authentication embodiments.

According to the present invention, the ridge pattern corresponding to the ridge area included in the recognition data (or the minutiae point of the ridge pattern extracted through the fingerprint recognition unit 715) is used as the identification means for identifying the user, The biohazard authenticated through at least one of the first through fourth authentication embodiments may be used as authentication means for authenticating that the identified user is an actual user.

The authentication result processing unit 755 combines the fingerprint matching result generated through the fingerprint matching unit 725 and the living body swallow authentication result recognized through the living body swallow authentication unit 750 and transmits the result through the sensor unit 705 And generates a final authentication result for the sensed user's body part. The authentication result processing unit 755 determines whether the fingerprint matching unit 725 successfully matches the ridge pattern of the fingerprint through the fingerprint matching unit 725 and the sensor unit 705 when the living body swallowing is authenticated through the living body swallow authentication unit 750. [ The final authentication result in which the body part of the user sensed through the biometrics authentication can be generated. Otherwise, the final authentication result corresponding to the authentication failure can be generated. The authentication result processing unit 755 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 biometric authentication result may be used as an authentication means for authenticating that the user is an actual user.

If the fingerprint recognition unit 715, the fingerprint storage unit 720 and the fingerprint matching unit 725 are omitted according to another embodiment of the present invention, the authentication result processing unit 755 may transmit the biometric data to the biometric authentication unit 750 The user can generate the final authentication result for the body part of the user sensed by the sensor unit 705 using the result of the body fat authentication generated through the authentication unit 705. The generated final authentication result is outputted through the designated means Or by a designated means via a designated path.

FIG. 8 is a diagram illustrating a process of authenticating living body sweating according to an embodiment of the present invention.

More specifically, in FIG. 8, N sensing data generated by sensing the user's body part N times in the specified ms unit using the sensor unit 705 capable of sensing fingerprints are matched based on the position of the pore on the fingerprint ridge (N-1) cumulative difference recognition data generated by subtracting the sensed value of the first recognition data from the sensed value of the (n + 1) The body part is authenticated by the actual living body sweating of the body part by discriminating the area and referring to FIG. 8 of the present invention. Referring to FIG. 8, It will be appreciated that various implementations of the invention (e.g., omitting some steps or altering the order) may be deduced, Is made, including the method, to which the technical feature that is not limited to the exemplary method shown in the figure 8.

Referring to FIG. 8, an apparatus 700 of the present invention acquires N sensed data obtained by sensing the user's specific body part N times in a specified ms unit through a sensor of the sensor unit 705 (800). 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 (805). 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 (810). On the other hand, if the user's fingerprint is 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 (815), 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, (825). Preferably, the apparatus 700 may generate recognition data (825) according to at least one of the first through fifth recognition data generation embodiments of the present invention.

The device 700 recognizes the position of the pores existing in the ridge area of each recognition data generated (830). Preferably, the apparatus 700 determines the base sensing value of the ridge region included in each recognition data through at least one of the first to fourth base sensing value determination embodiments of the present invention, 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 singularity identification embodiments, At least s sensing value singularity regions of the sensing value singularity region can be recognized as s pore positions (830).

The apparatus 700 matches the positions of the pores of the first recognition data and the (n + 1) -th recognition data among the recognition data (835). Preferably, the apparatus 700 may match the positions of the pores of the first recognition data and the (n + 1) -th recognition data using the geometric relationship of the pores.

The apparatus 700 subtracts the sensed value of the first recognition data from the sensed value of the (n + 1) recognition data that matches the position of the porthole to generate nth cumulative difference recognition data (840). Preferably, the apparatus 700 determines (but omits the base sensing value) the ridge area of the ridge area of the (n + 1) -th recognition data and the first recognition data, (N + 1) -th recognition data on the basis of the correction coefficient after calculating a correction coefficient for matching the recognition data with the base sensing value of the ridge area of the first recognition data, And the n-th accumulated difference recognition data may be generated (840).

The apparatus 700 performs a discrimination procedure for at least one cumulative difference recognition singularity area having a value equal to or larger than a specified threshold value on the ridge area of the n-th cumulative difference recognition data (step 845). Preferably, the apparatus 700 may perform the discrimination procedure for the cumulative difference recognition singularity area according to at least one of the first through fourth swallowing authentication embodiments of the present invention (845). Meanwhile, the process may be repeatedly performed on the (N-1) cumulative difference recognition data.

The apparatus 700 identifies at least one cumulative difference recognition singularity area generated on at least one cumulative difference recognition data among the (N-1) cumulative difference recognition data (850), if at least one cumulative difference recognition data The device 700 generates a biometric sweating authentication result obtained by authenticating the identified cumulative difference recognition singularity area with the living body sweating. On the other hand, if at least one cumulative difference recognition singularity area generated on at least one of the cumulative difference recognition data among (N-1) cumulative difference recognition data is not identified, the apparatus 700 may perform the bi- A result is generated (860).

The device 700 generates the final authentication result by combining the fingerprint matching result and the biometric FP authentication result, and outputs the generated final authentication result or provides the generated final authentication result to the designated path (870).

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: cumulative difference recognition data generating unit
750: vital signs authentication unit 755: authentication result processing unit

Claims (31)

A method for performing a method through an apparatus having a sensor unit capable of sensing a fingerprint,
A first step of acquiring N (N? 2) sensed data obtained by valid sensing the user's body part in a specified ms (millisecond) unit through the sensor unit;
A second step of reading sensing values of each sensing data to generate N pieces of recognition data capable of distinguishing ridges and valleys;
A third step of recognizing a position of a pore existing in a ridge area of each recognition data generated;
A fourth step of matching the first recognition data among the recognition data with the pore positions of the (n + 1) (1? N <N) recognition data;
(N-1) cumulative accumulation recognition data obtained by subtracting the sensed value of the first recognition data from the sensed value of the (n + 1) A fifth step of generating difference recognition data; And
Recognizing at least one cumulative difference recognition singularity area in which a value equal to or larger than a specified threshold value is generated on a longevity area of at least one cumulative difference recognition data among the generated (N-1) cumulative difference recognition data, And a sixth step of authenticating the living body.
The method according to claim 1,
Confirming a contact state of a user's body part through the sensor unit; And
And starting to acquire sensed data in which the user's body part is sensed effective in ms units from a certain time in the contact state of the body part.
The method according to claim 1,
Acquiring sensing data obtained by sensing a user's body part in contact with the sensor unit in units of ms; And
And starting to acquire sensing data sensed from a certain time after the sensed sensing data as effective sensing data.
The method according to claim 1,
Confirming contact release of the body part during effective sensing of the user's body part in contact with the sensor part in units of ms;
Determining whether a minimum sensing time set for authenticating the living body sweating has elapsed from the time when the validation of the body part is started upon confirming the disengagement of the body part;
And terminating the biofilm authentication process when the minimum sensing time has not elapsed.
The method according to claim 1,
Confirming an elapsed sensing time from a point at which effective sensing is started in units of ms in a user's body part in contact with the sensor unit;
Confirming whether the confirmed sensing time exceeds a maximum sensing time set for authenticating a living body sweating;
And performing a process of stopping the 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 region and a sensing value corresponding to the bony region are distinguishably corrected.
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 biomedical fingerprint authentication method comprises the steps of:
2. The method according to claim 1,
And generating recognition data by correcting at least one of a contrast, a contrast and a saturation of the sensed values so as to distinguish the ridge region and the bony region 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 bio-sweating authentication method comprises:
2. The method according to claim 1,
Determining a base sensing value of a ridge area included in each of the generated recognition data;
Identifying S (S? 3) sensing value singularity regions distributed on the ridge region using the basis sensing value; And
And a third step of recognizing at least s (3? S? S) sensing value singular points of the identified S sensed value singular point regions as s pore positions.
11. The method of claim 10,
Calculating a region distinguishing value for distinguishing the ridge region and the bony region from each other, and determining the region discrimination value as the base sensing value of the ridge region.
11. The method of claim 10,
Calculating a region distinguishing value for distinguishing the ridge region and the bony region from each other, and correcting the region distinguishing value to determine a base sensing value of the ridge region.
11. The method of claim 10,
Calculating a sensing value distribution of the ridge region and determining a value belonging to the designated distribution region on the sensing value distribution as the ridge region base sensing value.
11. The method of claim 10,
Calculating a sensing value distribution of the ridge region and correcting a value belonging to a designated distribution region on the sensing value distribution to determine a base sensing value of the ridge region.
11. The method of claim 10,
Calculating a mean value of sensing values of the ridge region and determining the sensed value as the ridge region sensing value.
11. The method of claim 10,
Calculating a sensing value average of the ridge area and correcting the sensing value average to determine a base sensing value of the ridge area.
11. The method of claim 10,
And identifying an area on the ridge area that is equal to or greater than a preset '+' contrast value that is lower than the base sensing value as a sensing value singularity area.
11. The method of claim 10,
Identifying a region in the ridge region that is less than or equal to a preset '-' contrast value than the base sensing value as a sensing value singularity region.
11. The method of claim 10,
Identifying a region including a region that is equal to or greater than a preset '+' contrast value in an area that is less than a predetermined '-' contrast value than the base sensing value on the ridge region as a sensing value singularity region. Sweating Authentication Method.
11. The method of claim 10,
Identifying a region including a region that is less than or equal to a preset '-' contrast value in a region that is greater than or equal to a predetermined '+' contrast value than the base sensing value on the ridge region as a sensing value singularity region. Sweating Authentication Method.
11. The method of claim 10,
Identifying a region in the ridge region that is greater than or equal to a predetermined '+' contrast value and a region that is less than or equal to a predetermined '-' contrast value as a sensing value focal point region; Vital authentication method.
The method as claimed in 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 first recognition data and the (n + 1) -th recognition data based on the geometrical relationship of the pores.
The method as claimed in claim 1,
Identifying minutiae points on the ridge pattern of the first recognition data and the (n + 1) 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 first recognition data and the (n + 1) -th recognition data based on the geometrical 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 for the ridge area of the (n + 1) -th recognition data with the base sensing value for the ridge area of the first recognition data,
The fifth step includes generating nth cumulative difference recognition data by subtracting the sensing value of the first recognition data from the sensing value of the (n + 1) recognition data based on the correction coefficient And a biometric authentication method.
The method according to claim 1,
Calculating an average of cumulative difference recognition values for a ridge region of the generated cumulative difference recognition data to determine an accumulated difference recognition base value of the ridge region,
The sixth step may include determining at least one cumulative difference recognition singularity area having a value greater than or equal to a specified threshold value based on the cumulative difference recognition base value to authenticate the actual body swallowing of the body part. A bioluminescence authentication method.
The method according to claim 1,
Calculating a cumulative difference recognition value distribution for the ridge area of the generated cumulative difference recognition data and determining a value belonging to the specified distribution area on the cumulative difference recognition value distribution as the cumulative difference recognition base value of the ridge area &Lt; / RTI &
The sixth step may include determining at least one cumulative difference recognition singularity area having a value greater than or equal to a specified threshold value based on the cumulative difference recognition base value to authenticate the actual body swallowing of the body part. A bioluminescence authentication method.
27. The method of claim 1, 25 or 26,
And a '+' threshold value or a '-' threshold value.
27. The method of claim 1, 25 or 26,
Wherein the biometric authentication information is set to a value proportional to the sensitivity of the sensor unit.
27. The method of claim 1, 25 or 26, wherein the cumulative difference-
Wherein the biofeedback authentication occurs in the form of '+' peaks or '-' peaks.
The method of claim 1, 25 or 26, wherein the cumulative difference recognition singularity area comprises:
And a region having a diameter of 200 mu m or less.
The method according to claim 1,
Wherein the ridge region is used as an identification means for identifying the user,
Wherein the swallowing authentication is used as an authentication means for authenticating that the user is an actual user.

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