CN117409488A - User identity recognition method, system, equipment and storage medium - Google Patents
User identity recognition method, system, equipment and storage medium Download PDFInfo
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- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
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- G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
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Abstract
The application relates to the technical field of identity recognition and discloses a user identity recognition method, a system, equipment and a storage medium, wherein the user identity recognition method comprises the steps of obtaining an identity recognition image and determining a face image from the identity recognition image; acquiring corresponding temperature sampling values from a plurality of preset temperature sampling points positioned in a face image area, numbering the temperature sampling values based on coordinate data of each temperature sampling point and a preset numbering rule, and generating a temperature sampling set; calculating the average value of all the temperature sampling values, defining the average value as a comparison temperature value, and calculating the temperature sampling variance based on each temperature sampling value and the comparison temperature value; when the comparison temperature value is in a preset temperature value interval and the temperature sampling variance is in a preset variance value interval, generating a face recognition instruction to recognize the face image; the face recognition method and device have the effect of improving face recognition identity verification efficiency.
Description
Technical Field
The present disclosure relates to the field of identity recognition technologies, and in particular, to a user identity recognition method, system, device, and storage medium.
Background
At present, when transacting financial business, user identity is often required to be verified; the current common method comprises the steps of verifying the equipment, the password, the identity card information and the mobile phone number of the user, however, the equipment, the password, the identity card information, the mobile phone number and the like of the user have the conditions of forgetting, losing, lending and the like, and whether the user operates personally is difficult to determine; at present, a plurality of financial institutions adopt a method for verifying biological information of a user, wherein the face recognition function only needs to be realized by a camera, a fingerprint recognition module or a high-precision camera for recognizing irises and the like are not needed, and the requirements on the user equipment are low, so that the method is widely used for improving the accuracy of user identity verification; however, the existing face recognition method needs to perform living body recognition on a recognition target so as to prevent lawbreakers from passing face recognition verification through a photo of a user or a mask based on the physical characteristics of the user; the current common living body identification methods comprise static living body identification and dynamic living body identification, and the living body identification modes require users to wait for lamplight to flash or require users to cooperate to make specific actions for living body identification, so that the efficiency of face identification is reduced.
Disclosure of Invention
In order to improve the efficiency of face recognition identity verification, the application provides a user identity recognition method, a system, equipment and a storage medium.
The first technical scheme adopted by the invention of the application is as follows:
a user identity recognition method, comprising:
acquiring an identity identification image, and determining a face image from the identity identification image;
acquiring corresponding temperature sampling values from a plurality of preset temperature sampling points positioned in a face image area, numbering the temperature sampling values based on coordinate data of each temperature sampling point and a preset numbering rule, and generating a temperature sampling set;
calculating the average value of all the temperature sampling values, defining the average value as a comparison temperature value, and calculating the temperature sampling variance based on each temperature sampling value and the comparison temperature value;
and when the comparison temperature value is in a preset temperature value interval and the temperature sampling variance is in a preset variance value interval, generating a face recognition instruction to recognize the face image.
By adopting the technical scheme, the identity recognition image shot by the camera is obtained, the face image is determined from the identity recognition image, the face recognition of the face image is facilitated to be carried out subsequently, and the coverage area of the face image is determined; carrying out temperature detection on a plurality of preset temperature sampling points positioned in a face image area to obtain corresponding temperature sampling values, numbering each temperature sampling value according to the positions of different temperature sampling points and preset numbering rules to generate a temperature sampling set so as to acquire the temperature value of each position in the face image; calculating average value according to each temperature sampling value to obtain a comparison temperature value so as to judge whether the face temperature of the human body is normal or not, and calculating variances of all the temperature sampling values in the temperature sampling set according to each temperature sampling value and the comparison temperature value to obtain temperature sampling variances; because human is a constant temperature animal, relatively stable body surface temperature can be maintained in any environment, on the other hand, because a plurality of organs exist on the face of the human body and protruding and sunken parts exist, small temperature difference exists on the surface temperature of the skin of different organs and different areas in the face image of the human body, when the face image is from a real living face, the numerical value of a comparison temperature value is located in a normal human body surface temperature range, the numerical value of a temperature sampling variance is located in a variance value range so as to carry out living body recognition on the face image, further carry out face recognition on the face image after living body recognition, and do not need to make specific actions by a user or carry out living body recognition by using lamplight flicker, thereby improving the efficiency of face recognition identity verification.
In a preferred example, the present application: an a row and a b row of temperature sampling points arranged in a square matrix are arranged in the shooting range of the identity identification image; the numbering rules are as follows: the temperature sampling points in the face image area from top to bottom are organized into a 1 st row, a 2 nd row and a 3 rd row … … m th row, and the temperature sampling points in the face image area from left to right are organized into a 1 st row, a 2 nd row and a 3 rd row … … n row, m is less than or equal to a, and n is less than or equal to b;
the step of numbering each temperature sampling value based on the coordinate data of each temperature sampling point and a preset numbering rule to generate a temperature sampling set comprises the following steps:
numbering each temperature sampling value based on the coordinate data of each temperature sampling point and a preset numbering rule;
generating a temperature sampling column group based on a plurality of temperature sampling values and numbers corresponding to each column of temperature sampling points;
a temperature sample set is generated based on each set of temperature sample columns.
By adopting the technical scheme, a plurality of temperature sampling points which are arranged in a square matrix are arranged in the shooting range of the identity recognition image so as to acquire the temperature values of all positions in the face image area, after numbering is carried out on all temperature sampling values according to the coordinate data of all temperature sampling points and a preset numbering rule, the temperature sampling values corresponding to all columns of temperature sampling points and the numbering are summarized to generate a temperature sampling column group so as to analyze the temperature difference rule and the characteristics of all columns of temperature sampling points in the face image subsequently; a temperature sample set is generated based on the number of temperature sample column groups.
In a preferred example, the present application: before generating the face recognition instruction to perform face recognition on the face image, the method further includes:
defining a temperature sampling column group with the largest temperature sampling value as a characteristic column group, and calculating the temperature difference characteristic value of the characteristic column group;
comparing the temperature difference characteristic value with a preset temperature difference characteristic value interval, and generating an active identification passing instruction if the temperature difference characteristic value is positioned in the temperature difference characteristic value interval;
wherein, set up the firstThe temperature sampling value of the row i and the column j is F i，j The ith column has x temperature sampling values, then the column characteristic value of the jth column And the temperature difference characteristic value takes the average value of the characteristic values.
By adopting the technical scheme, the temperature sampling column group with the largest temperature sampling value number is selected from the temperature sampling set and defined as the characteristic column group, the temperature difference characteristic value of the characteristic column group is calculated, if a plurality of temperature sampling value values in the same temperature sampling column are similar or have gradual change trend, the corresponding column characteristic value and the temperature difference characteristic value are smaller, and if a plurality of temperature sampling value values in the same temperature sampling column have fluctuation change, the corresponding column characteristic value and the temperature difference characteristic value are larger; comparing the temperature difference characteristic value with a preset temperature difference characteristic value interval so as to judge whether the temperature difference characteristic value is in a normal human face temperature difference characteristic range interval, and if so, generating a living body identification passing instruction to determine that the human face image accords with living body characteristics.
In a preferred example, the present application: when the comparison temperature value is located in a preset temperature value interval, and the temperature sampling variance is located in a preset variance value interval, before generating a face recognition instruction to perform face recognition on the face image, the face recognition method further comprises the following steps:
acquiring a plurality of experimental face images and corresponding experimental temperature sampling sets;
calculating corresponding experimental comparison temperature values and experimental temperature sampling variances based on the experimental temperature sampling sets;
determining an endpoint value of a temperature value interval based on the value of each experimental comparison temperature value, and determining an endpoint value of a variance value interval based on the value of each experimental temperature sampling variance;
the experimental face image comprises face images obtained by carrying out face recognition image acquisition on a plurality of different types of personnel in a plurality of different temperature environments.
By adopting the technical scheme, a large number of experimental face images and corresponding experimental temperature sampling sets are acquired by carrying out face recognition image acquisition on a plurality of different types of personnel in a plurality of different temperature environments, so that the temperature difference distribution rules of different positions on the normal living face can be analyzed conveniently; calculating a corresponding experimental comparison temperature value and experimental temperature sampling variance based on each experimental temperature sampling set so as to analyze conventional values of the comparison temperature value and the temperature sampling variance in normal face recognition; and counting according to the values of the temperature values of the experiment comparison, determining the end point value of the temperature value interval, counting according to the values of the variance of the temperature difference sampling of the experiment comparison, and determining the end point value of the variance value interval so as to improve the rationality of the temperature value interval and the value setting of the variance value interval.
In a preferred example, the present application: after the plurality of experimental face images and the corresponding experimental temperature sampling sets are obtained, the method further comprises the following steps:
calculating corresponding experimental column characteristic values based on the characteristic column groups in each experimental temperature sampling set;
and determining the endpoint value of the temperature difference characteristic value interval based on the value of each experimental column characteristic value.
By adopting the technical scheme, the corresponding experimental column characteristic values are calculated based on the characteristic column groups in each experimental temperature sampling set so as to analyze the conventional numerical values of the column characteristic values in normal face recognition; and counting according to the values of the characteristic values of a plurality of experimental columns, and determining the endpoint values of the temperature difference characteristic value interval so as to improve the reasonability of the numerical setting of the Gao Wencha characteristic value interval.
The second object of the present application is achieved by the following technical scheme:
a user identity recognition system, which is applied to any one of the user identity recognition methods, comprising:
the face image acquisition module is used for acquiring an identity recognition image and determining a face image from the identity recognition image;
the temperature sampling set generation module is used for acquiring corresponding temperature sampling values from a plurality of preset temperature sampling points positioned in the face image area, numbering the temperature sampling values based on coordinate data of the temperature sampling points and a preset numbering rule, and generating a temperature sampling set;
The temperature sampling variance calculation module is used for calculating the average value of all the temperature sampling values, defining the average value as a comparison temperature value and calculating the temperature sampling variance based on each temperature sampling value and the comparison temperature value;
and the living body identification module is used for generating a face identification instruction to identify the face image when the comparison temperature value is in a preset temperature value interval and the temperature sampling variance is in a preset variance value interval.
By adopting the technical scheme, the identity recognition image shot by the camera is obtained, the face image is determined from the identity recognition image, the face recognition of the face image is facilitated to be carried out subsequently, and the coverage area of the face image is determined; carrying out temperature detection on a plurality of preset temperature sampling points positioned in a face image area to obtain corresponding temperature sampling values, numbering each temperature sampling value according to the positions of different temperature sampling points and preset numbering rules to generate a temperature sampling set so as to acquire the temperature value of each position in the face image; calculating average value according to each temperature sampling value to obtain a comparison temperature value so as to judge whether the face temperature of the human body is normal or not, and calculating variances of all the temperature sampling values in the temperature sampling set according to each temperature sampling value and the comparison temperature value to obtain temperature sampling variances; because human is a constant temperature animal, relatively stable body surface temperature can be maintained in any environment, on the other hand, because a plurality of organs exist on the face of the human body and protruding and sunken parts exist, small temperature difference exists on the surface temperature of the skin of different organs and different areas in the face image of the human body, when the face image is from a real living face, the numerical value of a comparison temperature value is located in a normal human body surface temperature range, the numerical value of a temperature sampling variance is located in a variance value range so as to carry out living body recognition on the face image, further carry out face recognition on the face image after living body recognition, and do not need to make specific actions by a user or carry out living body recognition by using lamplight flicker, thereby improving the efficiency of face recognition identity verification.
In a preferred example, the present application: the temperature sampling set generating module further includes:
the temperature sampling value coding sub-module is used for numbering each temperature sampling value based on the coordinate data of each temperature sampling point and a preset numbering rule;
the temperature sampling column group generation submodule is used for generating a temperature sampling column group based on a plurality of temperature sampling values and numbers corresponding to each column of temperature sampling points;
the temperature sampling set determination submodule is used for generating a temperature sampling set based on each temperature sampling column group.
By adopting the technical scheme, a plurality of temperature sampling points which are arranged in a square matrix are arranged in the shooting range of the identity recognition image so as to acquire the temperature values of all positions in the face image area, after numbering is carried out on all temperature sampling values according to the coordinate data of all temperature sampling points and a preset numbering rule, the temperature sampling values corresponding to all columns of temperature sampling points and the numbering are summarized to generate a temperature sampling column group so as to analyze the temperature difference rule and the characteristics of all columns of temperature sampling points in the face image subsequently; a temperature sample set is generated based on the number of temperature sample column groups.
In a preferred example, the present application: the living body identification module further includes:
The temperature difference characteristic value calculation sub-module is used for defining a temperature sampling column group with the largest temperature sampling value as a characteristic column group and calculating the temperature difference characteristic value of the characteristic column group;
and the living body identification passing instruction generation sub-module is used for comparing the temperature difference characteristic value with a preset temperature difference characteristic value interval, and generating a living body identification passing instruction if the temperature difference characteristic value is positioned in the temperature difference characteristic value interval.
By adopting the technical scheme, the temperature sampling column group with the largest temperature sampling value number is selected from the temperature sampling set and defined as the characteristic column group, the temperature difference characteristic value of the characteristic column group is calculated, if a plurality of temperature sampling value values in the same temperature sampling column are similar or have gradual change trend, the corresponding column characteristic value and the temperature difference characteristic value are smaller, and if a plurality of temperature sampling value values in the same temperature sampling column have fluctuation change, the corresponding column characteristic value and the temperature difference characteristic value are larger; comparing the temperature difference characteristic value with a preset temperature difference characteristic value interval so as to judge whether the temperature difference characteristic value is in a normal human face temperature difference characteristic range interval, and if so, generating a living body identification passing instruction to determine that the human face image accords with living body characteristics.
The third object of the present application is achieved by the following technical scheme:
a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the user identification method described above when the computer program is executed.
The fourth object of the present application is achieved by the following technical scheme:
a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the user identification method described above.
In summary, the present application includes at least one of the following beneficial technical effects:
1. acquiring an identity recognition image shot by a camera, determining a face image from the identity recognition image, facilitating the subsequent face recognition of the face image, and determining the coverage area of the face image; carrying out temperature detection on a plurality of preset temperature sampling points positioned in a face image area to obtain corresponding temperature sampling values, numbering each temperature sampling value according to the positions of different temperature sampling points and preset numbering rules to generate a temperature sampling set so as to acquire the temperature value of each position in the face image; calculating average value according to each temperature sampling value to obtain a comparison temperature value so as to judge whether the face temperature of the human body is normal or not, and calculating variances of all the temperature sampling values in the temperature sampling set according to each temperature sampling value and the comparison temperature value to obtain temperature sampling variances; because human is a constant temperature animal, relatively stable body surface temperature can be maintained in any environment, on the other hand, because a plurality of organs exist on the face of the human body and protruding and sunken parts exist, small temperature difference exists on the surface temperature of the skin of different organs and different areas in the face image of the human body, when the face image is from a real living face, the numerical value of a comparison temperature value is located in a normal human body surface temperature range, the numerical value of a temperature sampling variance is located in a variance value range so as to carry out living body recognition on the face image, further carry out face recognition on the face image after living body recognition, and do not need to make specific actions by a user or carry out living body recognition by using lamplight flicker, thereby improving the efficiency of face recognition identity verification.
2. Setting a plurality of temperature sampling points which are arranged in a square matrix in a shooting range of an identity identification image so as to acquire temperature values of all positions in a face image area, numbering all temperature sampling values according to coordinate data of all the temperature sampling points and a preset numbering rule, and summarizing the temperature sampling values corresponding to all the rows of the temperature sampling points and the numbering to generate a temperature sampling row group so as to analyze temperature difference rules and characteristics of all the rows of the temperature sampling points in the face image; a temperature sample set is generated based on the number of temperature sample column groups.
3. Selecting a temperature sampling column group with the largest temperature sampling value number from the temperature sampling set, defining the temperature sampling column group as a characteristic column group, calculating the temperature difference characteristic value of the characteristic column group, if a plurality of temperature sampling value values in the same temperature sampling column are similar or have gradual change trend, the corresponding column characteristic value and the temperature difference characteristic value are smaller, and if a plurality of temperature sampling value values in the same temperature sampling column have fluctuation change, the corresponding column characteristic value and the temperature difference characteristic value are larger; comparing the temperature difference characteristic value with a preset temperature difference characteristic value interval so as to judge whether the temperature difference characteristic value is in a normal human face temperature difference characteristic range interval, and if so, generating a living body identification passing instruction to determine that the human face image accords with living body characteristics.
Drawings
Fig. 1 is a flowchart of a user identification method in accordance with an embodiment of the present application.
Fig. 2 is a schematic diagram of temperature sampling point distribution in an embodiment of the present application.
Fig. 3 is a schematic block diagram of a user identification system according to a second embodiment of the present application.
Fig. 4 is a schematic view of an apparatus in a third embodiment of the present application.
Detailed Description
The present application is described in further detail below in conjunction with figures 1 to 4.
Example 1
Referring to fig. 1, the application discloses a user identity recognition method, which can be used for improving the efficiency of living body detection when a user performs face recognition, and specifically comprises the following steps:
s10: and acquiring an identification image, and determining a face image from the identification image.
In this embodiment, the identification image refers to an image captured by an imaging component of a terminal device, where the terminal device may be a device equipped with a face recognition camera and a temperature detection sensor, and is particularly suitable for a terminal device that needs to identify a user identity, such as an intelligent ATM, an intelligent teller machine, a self-service business transaction machine, and the like; the face image refers to an image of the face of the user determined from the identification image.
Specifically, an identification image shot by a camera of the terminal equipment is acquired, a face image is further determined from the identification image, the face image is conveniently and subsequently identified, and the coverage area of the face image is determined, so that the temperature detection is subsequently carried out on each position in the face image of the user, and further living body detection is carried out.
S20: and acquiring corresponding temperature sampling values from a plurality of preset temperature sampling points positioned in the face image area, numbering the temperature sampling values based on coordinate data of each temperature sampling point and a preset numbering rule, and generating a temperature sampling set.
In the embodiment, a row a and b temperature sampling points arranged in a square matrix are arranged in the shooting range of the identification image; the coordinate data of the temperature sampling points are specifically formed by serial numbers of rows and columns where the temperature sampling points are located, and the preset numbering rule is as follows: the temperature sampling points in the face image area from top to bottom are organized into a 1 st row, a 2 nd row and a 3 rd row … … m th row, and the temperature sampling points in the face image area from left to right are organized into a 1 st row, a 2 nd row and a 3 rd row … … n row, m is less than or equal to a, and n is less than or equal to b; as shown in fig. 2, 1 is a shooting range of an identification image, 2 is a temperature sampling point, and 3 is a contour of a face image.
The number of the temperature detection sensors of the terminal equipment can be one or more, the temperature detection sensors can be non-contact single-point temperature measurement sensors, such as infrared temperature measurement sensors, non-contact surface temperature measurement sensors, such as thermal imaging sensors, and the like, when the temperature detection is carried out on each temperature sampling point, the temperature detection sensors can be used for detecting the temperature sampling values of all the temperature sampling points at one time, and the temperature sampling values of each temperature sampling point can be detected in a divided manner, so that a person skilled in the art knows how to select a proper type of sensor according to the factors such as the computer performance, the face recognition speed, the cost budget and the like of the terminal equipment; preferably, the temperature detection sensor capable of detecting all temperature sampling points at one time is selected, so that the living experience verification efficiency is high.
Specifically, temperature values of all temperature sampling points in a face image area are detected through a temperature detection sensor, all temperature sampling values corresponding to the temperature sampling points in the face image area are obtained, and a temperature sampling set is generated after numbering is carried out on all the temperature sampling values according to positions of different temperature sampling points and preset numbering rules so as to obtain the temperature values of all the positions in the face image.
Wherein, in step S20, it includes:
s21: and numbering the temperature sampling values based on the coordinate data of the temperature sampling points and a preset numbering rule.
Specifically, a plurality of temperature sampling points which are arranged in a square matrix are arranged in a shooting range of the identity identification image so as to collect temperature values of all positions in a face image area, and the temperature sampling values are numbered according to coordinate data of all the temperature sampling points and a preset numbering rule.
S22: and generating a temperature sampling column group based on a plurality of temperature sampling values and numbers corresponding to each column of temperature sampling points.
In this embodiment, the temperature sampling column group refers to a data group generated based on a temperature sampling value and a number corresponding to a temperature sampling point located in a face image area.
Specifically, after numbering each temperature sampling value located in the face image area, the temperature sampling value corresponding to each column of temperature sampling points and the number are summarized to generate a temperature sampling column group so as to analyze the temperature difference rule and the characteristics of each column of temperature sampling points in the face image.
Further, the temperature sampling row group can be generated based on a plurality of temperature sampling values and numbers corresponding to each row of temperature sampling points according to actual requirements, and the specific method can refer to a generation scheme of the temperature sampling row group.
S23: a temperature sample set is generated based on each set of temperature sample columns.
Specifically, a temperature sample set is generated based on a number of temperature sample column groups.
S30: the average value of all the temperature sampling values is calculated and defined as a comparison temperature value, and the temperature sampling variance is calculated based on each temperature sampling value and the comparison temperature value.
In this embodiment, the temperature sampling variance refers to a variance obtained for each temperature sampling value in the temperature sampling set by taking the comparison temperature value as a mean value.
Specifically, an average value is calculated according to each temperature sampling value to obtain a comparison temperature value so as to judge whether the face temperature of the human body is normal or not, and variances of all the temperature sampling values in the temperature sampling set are calculated according to each temperature sampling value and the comparison temperature value so as to obtain temperature sampling variances.
S40: and when the comparison temperature value is in a preset temperature value interval and the temperature sampling variance is in a preset variance value interval, generating a face recognition instruction to recognize the face image.
In this embodiment, the temperature value interval refers to a value interval for comparing with a comparison temperature value, and is used to determine whether the surface temperature of the face of the person corresponding to the face image is in a normal range; preferably, the temperature value interval can be set to 20-37 ℃, wherein the lower limit of the temperature value interval can be adjusted according to the current environment temperature so as to reduce the situation of frequent liveness experience failure caused by the too low environment temperature; the variance value interval is a numerical value interval for comparing with the temperature sampling variance, and is used for judging whether the temperature difference of each position in the face image accords with the temperature difference condition of a normal face.
In particular, since the human being is a warm-blooded animal, a relatively stable body surface temperature is maintained in any environment, and thus the average temperature of the human face should be within a normal body surface temperature range; on the other hand, because a plurality of organs exist on the face of the human body and protruding and sunken parts exist, the surface temperatures of the skin of different organs and different areas in the face image of the human body are caused to have small temperature differences, and the common user photo or special mask presented by carriers such as paper, board, electronic screen and the like of lawless persons are provided with the characteristic that the temperature differences of the whole area of the face image are small, therefore, when the face image is from a real living face, the numerical value of a comparison temperature value is in a normal face temperature interval, the numerical value of a temperature sampling variance is in a variance value interval, and both the too large temperature sampling variance and the too small temperature sampling variance are not in accordance with the face temperature characteristics of the living face, so that living face image is conveniently identified, the face image after living body identification is further identified, the face image identification method of the face image can be adopted, the face comparison method in the prior art is not repeated, and the face image of one or more persons in the user image library can be compared according to one-to-one face image of the user image, so that one-to-one or one-to-more practical functions are realized; because the user does not need to make specific actions and does not need to use lamplight flicker to carry out living body identification, the efficiency of face identification and identity verification is improved.
Wherein, before step S40, it includes:
s41: and defining the temperature sampling column group with the largest temperature sampling value as a characteristic column group, and calculating the temperature difference characteristic value of the characteristic column group.
Because the electronic equipment has the condition of local heating, when the photo presented by taking the electronic screen as a carrier is used for face recognition, the condition that the temperature sampling variance accords with the variance value interval is likely to occur, and thus the condition that the result of the active recognition is wrong is likely to occur; however, as the regions such as forehead, eye sockets, nose, nostrils, mouth, chin and the like are sequentially arranged from the forehead to the chin of a normal living human face, the different regions have differences in the richness of subcutaneous tissue capillaries, and the partial regions are different from the approaches of the gases and body fluids in the human body, so that obvious temperature fluctuation change characteristics exist, even if the temperature sampling variance of articles such as photos or special masks, head covers and the like presented by carriers such as paper, plates, electronic screens and the like accords with the variance value interval, the temperature fluctuation change characteristics cannot exist, and if the temperature fluctuation change characteristics of the human face image are added into the judgment standard of living body verification, the accuracy of living experience can be further improved.
Specifically, let the temperature sampling value in the ith row and j column be F i，j The ith column has x temperature sampling values, then the column characteristic value of the jth column The temperature difference characteristic value takes the average value of the characteristic values so as to adapt to the scene when a plurality of characteristic column groups exist.
Specifically, a temperature sampling column group with the maximum number of temperature sampling values is selected from the temperature sampling set and defined as a characteristic column group, wherein the number of the characteristic column groups can be determined according to the density of actual temperature sampling points, and the preferable scheme is that 50% of the area of the forehead, the eye sockets, the nose, the nostrils, the mouth and the chin can be covered; and calculating the temperature difference characteristic value of the characteristic array group, wherein if the values of a plurality of temperature sampling values in the same temperature sampling array are similar or gradually changed, the values of the corresponding array characteristic value and the temperature difference characteristic value are smaller, and if the values of a plurality of temperature sampling values in the same temperature sampling array have fluctuation, the values of the corresponding array characteristic value and the temperature difference characteristic value are larger.
S42: and comparing the temperature difference characteristic value with a preset temperature difference characteristic value interval, and generating an active identification passing instruction if the temperature difference characteristic value is positioned in the temperature difference characteristic value interval.
Specifically, comparing the temperature difference characteristic value with a preset temperature difference characteristic value interval so as to judge whether the temperature difference characteristic value is in a normal human face temperature difference characteristic range interval, if so, generating a living body recognition passing instruction to determine that the human face image accords with living body characteristics, and if not, not generating a living body recognition passing instruction to prevent the generation of the human face recognition instruction.
Specifically, the living body recognition passing instruction is a precondition for generating the face recognition instruction subsequently, and if the living body recognition passing instruction does not exist, the face recognition instruction cannot be generated even if the comparison temperature value is within the temperature value interval and the temperature sampling variance is within the variance value interval.
Before step S40, the user identification method further includes:
s51: and acquiring a plurality of experimental face images and corresponding experimental temperature sampling sets.
In this embodiment, the experimental face image includes face images obtained by performing face recognition image acquisition on a plurality of different types of people in a plurality of different temperature environments, where the different types of people may specifically include people of different sexes and different age groups, so as to reduce the failure rate of live experience.
Specifically, face recognition image acquisition is performed on a plurality of different types of people in a plurality of different temperature environments, so that a large number of experimental face images and corresponding experimental temperature sampling sets are obtained, and the temperature difference distribution rules of different positions on the normal living face can be analyzed conveniently.
S52: and calculating corresponding experimental comparison temperature values and experimental temperature sampling variances based on each experimental temperature sampling set.
Specifically, the corresponding experimental comparison temperature value and experimental temperature sampling variance are calculated based on each experimental temperature sampling set so as to analyze the conventional numerical values of the comparison temperature value and the temperature sampling variance in normal face recognition.
S53: and determining the end point value of the temperature value interval based on the value of each experimental comparison temperature value, and determining the end point value of the variance value interval based on the value of each experimental temperature sampling variance.
Specifically, the end point value of the temperature value interval is determined after statistics is carried out according to the values of the temperature values of the experiment comparison, and the end point value of the variance value interval is determined after statistics is carried out according to the values of the variance of the temperature difference sampling of the experiment comparison, so that the rationality of the temperature value interval and the variance value interval value setting is improved.
Wherein, after step S51, further comprising:
s54: and calculating corresponding experimental column characteristic values based on the characteristic column groups in each experimental temperature sampling set.
Specifically, the corresponding experimental column characteristic values are calculated based on the characteristic column groups in each experimental temperature sampling set, so that the conventional numerical values of the column characteristic values in normal face recognition are analyzed.
S55: and determining the endpoint value of the temperature difference characteristic value interval based on the value of each experimental column characteristic value.
Specifically, the endpoint values of the temperature difference characteristic value interval are determined after statistics is carried out according to the values of the characteristic values of a plurality of experimental columns, so as to improve the rationality of the numerical setting of the Gao Wencha characteristic value interval.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic of each process, and should not limit the implementation process of the embodiment of the present application in any way.
Example two
A user identification system corresponding to the user identification method in the above embodiment.
As shown in fig. 3, the user identification system includes a face image acquisition module, a temperature sampling set generation module, a temperature sampling variance calculation module, and a living body identification module. The detailed description of each functional module is as follows:
the face image acquisition module is used for acquiring an identity recognition image and determining a face image from the identity recognition image;
the temperature sampling set generation module is used for acquiring corresponding temperature sampling values from a plurality of preset temperature sampling points positioned in the face image area, numbering the temperature sampling values based on coordinate data of the temperature sampling points and a preset numbering rule, and generating a temperature sampling set;
The temperature sampling variance calculation module is used for calculating the average value of all the temperature sampling values, defining the average value as a comparison temperature value and calculating the temperature sampling variance based on each temperature sampling value and the comparison temperature value;
and the living body identification module is used for generating a face identification instruction to identify the face image when the comparison temperature value is in a preset temperature value interval and the temperature sampling variance is in a preset variance value interval.
Wherein, temperature sampling set generation module still includes:
the temperature sampling value coding sub-module is used for numbering each temperature sampling value based on the coordinate data of each temperature sampling point and a preset numbering rule;
the temperature sampling column group generation submodule is used for generating a temperature sampling column group based on a plurality of temperature sampling values and numbers corresponding to each column of temperature sampling points;
the temperature sampling set determination submodule is used for generating a temperature sampling set based on each temperature sampling column group.
Wherein the living body identification module further includes:
the temperature difference characteristic value calculation sub-module is used for defining a temperature sampling column group with the largest temperature sampling value as a characteristic column group and calculating the temperature difference characteristic value of the characteristic column group;
And the living body identification passing instruction generation sub-module is used for comparing the temperature difference characteristic value with a preset temperature difference characteristic value interval, and generating a living body identification passing instruction if the temperature difference characteristic value is positioned in the temperature difference characteristic value interval.
Wherein, the user identification system further includes:
the experimental data acquisition module is used for acquiring a plurality of experimental face images and corresponding experimental temperature sampling sets;
the first experimental data calculation module is used for calculating corresponding experimental comparison temperature values and experimental temperature sampling variances based on each experimental temperature sampling set;
the first experimental result generation module is used for determining the endpoint value of the temperature value interval based on the value of each experimental comparison temperature value and determining the endpoint value of the variance value interval based on the value of each experimental temperature sampling variance;
the second experimental data calculation module is used for calculating corresponding experimental column characteristic values based on the characteristic column groups in each experimental temperature sampling set;
and the second experimental result generation module is used for determining endpoint values of the temperature difference characteristic value interval based on the values of the characteristic values of each experimental column.
For specific limitations of the user identification system, reference may be made to the above limitation of the user identification method, and no further description is given here; all or part of each module in the user identity recognition system can be realized by software, hardware and the combination thereof; the above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Example III
A computer device, which may be a server, may have an internal structure as shown in fig. 4. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing data such as identity identification images, face images, temperature sampling values, coordinate data, numbering rules, temperature sampling sets, comparison temperature values, temperature sampling variances, temperature value intervals, variance value intervals, face identification instructions and the like. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by the processor to implement a user identification method.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
S10: acquiring an identity identification image, and determining a face image from the identity identification image;
s20: acquiring corresponding temperature sampling values from a plurality of preset temperature sampling points positioned in a face image area, numbering the temperature sampling values based on coordinate data of each temperature sampling point and a preset numbering rule, and generating a temperature sampling set;
s30: calculating the average value of all the temperature sampling values, defining the average value as a comparison temperature value, and calculating the temperature sampling variance based on each temperature sampling value and the comparison temperature value;
s40: and when the comparison temperature value is in a preset temperature value interval and the temperature sampling variance is in a preset variance value interval, generating a face recognition instruction to recognize the face image.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
s10: acquiring an identity identification image, and determining a face image from the identity identification image;
s20: acquiring corresponding temperature sampling values from a plurality of preset temperature sampling points positioned in a face image area, numbering the temperature sampling values based on coordinate data of each temperature sampling point and a preset numbering rule, and generating a temperature sampling set;
S30: calculating the average value of all the temperature sampling values, defining the average value as a comparison temperature value, and calculating the temperature sampling variance based on each temperature sampling value and the comparison temperature value;
s40: and when the comparison temperature value is in a preset temperature value interval and the temperature sampling variance is in a preset variance value interval, generating a face recognition instruction to recognize the face image.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink), DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand; the technical scheme described in the foregoing embodiments can be modified or some of the features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. A method for identifying a user, comprising:
acquiring an identity identification image, and determining a face image from the identity identification image;
acquiring corresponding temperature sampling values from a plurality of preset temperature sampling points positioned in a face image area, numbering the temperature sampling values based on coordinate data of each temperature sampling point and a preset numbering rule, and generating a temperature sampling set;
Calculating the average value of all the temperature sampling values, defining the average value as a comparison temperature value, and calculating the temperature sampling variance based on each temperature sampling value and the comparison temperature value;
and when the comparison temperature value is in a preset temperature value interval and the temperature sampling variance is in a preset variance value interval, generating a face recognition instruction to recognize the face image.
2. The method for identifying a user according to claim 1, wherein: an a row and a b row of temperature sampling points arranged in a square matrix are arranged in the shooting range of the identity identification image; the numbering rules are as follows: the temperature sampling points in the face image area from top to bottom are organized into a 1 st row, a 2 nd row and a 3 rd row … … m th row, and the temperature sampling points in the face image area from left to right are organized into a 1 st row, a 2 nd row and a 3 rd row … … n row, m is less than or equal to a, and n is less than or equal to b;
the step of numbering each temperature sampling value based on the coordinate data of each temperature sampling point and a preset numbering rule to generate a temperature sampling set comprises the following steps:
numbering each temperature sampling value based on the coordinate data of each temperature sampling point and a preset numbering rule;
generating a temperature sampling column group based on a plurality of temperature sampling values and numbers corresponding to each column of temperature sampling points;
A temperature sample set is generated based on each set of temperature sample columns.
3. A method of user identification as claimed in claim 2, wherein: before generating the face recognition instruction to perform face recognition on the face image, the method further includes:
defining a temperature sampling column group with the largest temperature sampling value as a characteristic column group, and calculating the temperature difference characteristic value of the characteristic column group;
comparing the temperature difference characteristic value with a preset temperature difference characteristic value interval, and generating an active identification passing instruction if the temperature difference characteristic value is positioned in the temperature difference characteristic value interval;
wherein, the temperature sampling value of the ith row and the j columns is F i,j The ith column has x temperature sampling values, then the column characteristic value of the jth columnAnd x is less than or equal to n, and the temperature difference characteristic value is an average value of the characteristic values.
4. A method of user identification according to claim 1 or 3, characterized in that: when the comparison temperature value is located in a preset temperature value interval, and the temperature sampling variance is located in a preset variance value interval, before generating a face recognition instruction to perform face recognition on the face image, the face recognition method further comprises the following steps:
acquiring a plurality of experimental face images and corresponding experimental temperature sampling sets;
Calculating corresponding experimental comparison temperature values and experimental temperature sampling variances based on the experimental temperature sampling sets;
determining an endpoint value of a temperature value interval based on the value of each experimental comparison temperature value, and determining an endpoint value of a variance value interval based on the value of each experimental temperature sampling variance;
the experimental face image comprises face images obtained by carrying out face recognition image acquisition on a plurality of different types of personnel in a plurality of different temperature environments.
5. The method for identifying a user according to claim 4, wherein: after the plurality of experimental face images and the corresponding experimental temperature sampling sets are obtained, the method further comprises the following steps:
calculating corresponding experimental column characteristic values based on the characteristic column groups in each experimental temperature sampling set;
and determining the endpoint value of the temperature difference characteristic value interval based on the value of each experimental column characteristic value.
6. A user identification system, characterized in that it is applied to the user identification method as claimed in any one of claims 1 to 5, comprising:
the face image acquisition module is used for acquiring an identity recognition image and determining a face image from the identity recognition image;
The temperature sampling set generation module is used for acquiring corresponding temperature sampling values from a plurality of preset temperature sampling points positioned in the face image area, numbering the temperature sampling values based on coordinate data of the temperature sampling points and a preset numbering rule, and generating a temperature sampling set;
the temperature sampling variance calculation module is used for calculating the average value of all the temperature sampling values, defining the average value as a comparison temperature value and calculating the temperature sampling variance based on each temperature sampling value and the comparison temperature value;
and the living body identification module is used for generating a face identification instruction to identify the face image when the comparison temperature value is in a preset temperature value interval and the temperature sampling variance is in a preset variance value interval.
7. A subscriber identity system according to claim 6, wherein: the temperature sampling set generating module further includes:
the temperature sampling value coding sub-module is used for numbering each temperature sampling value based on the coordinate data of each temperature sampling point and a preset numbering rule;
the temperature sampling column group generation submodule is used for generating a temperature sampling column group based on a plurality of temperature sampling values and numbers corresponding to each column of temperature sampling points;
The temperature sampling set determination submodule is used for generating a temperature sampling set based on each temperature sampling column group.
8. A subscriber identity system according to claim 7, wherein: the living body identification module further includes:
the temperature difference characteristic value calculation sub-module is used for defining a temperature sampling column group with the largest temperature sampling value as a characteristic column group and calculating the temperature difference characteristic value of the characteristic column group;
and the living body identification passing instruction generation sub-module is used for comparing the temperature difference characteristic value with a preset temperature difference characteristic value interval, and generating a living body identification passing instruction if the temperature difference characteristic value is positioned in the temperature difference characteristic value interval.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the user identification method according to any of claims 1 to 5 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the user identification method according to any one of claims 1 to 5.
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