CN112084893A

CN112084893A - Biological recognition terminal abnormity detection method, device, equipment and storage medium

Info

Publication number: CN112084893A
Application number: CN202010856967.5A
Authority: CN
Inventors: 窦逸辛; 柴培林; 赖嘉伟; 卞凯; 傅宜生
Original assignee: China Unionpay Co Ltd
Current assignee: China Unionpay Co Ltd
Priority date: 2020-08-24
Filing date: 2020-08-24
Publication date: 2020-12-15
Anticipated expiration: 2040-08-24
Also published as: CN112084893B

Abstract

The application discloses a method, a device, equipment and a storage medium for detecting abnormality of a biological recognition terminal, and belongs to the field of data processing. The method comprises the following steps: acquiring historical identification data of a target biological identification terminal, wherein the historical identification data comprises first biological characteristic data and a similarity parameter value of the first biological characteristic data and sample biological characteristic data in an identification database; obtaining a first distribution characteristic associated with first biological characteristic data under each similarity parameter value based on historical identification data; and determining that the target biological identification terminal is abnormal under the condition that the first distribution characteristic does not accord with any preset standard distribution condition, wherein the standard distribution condition is related to the distribution characteristics of the second biological characteristic data of the biological identification terminal which is not abnormal under each similarity parameter value. According to the embodiment of the application, the abnormal biological identification terminal can be detected.

Description

Biological recognition terminal abnormity detection method, device, equipment and storage medium

Technical Field

The application belongs to the field of data processing, and particularly relates to a method, a device, equipment and a storage medium for detecting abnormality of a biometric identification terminal.

Background

The biometric technology is a technology for performing identity authentication by using biometric features. The applicable scenarios of biometric identification are increasing, for example, transaction scenarios, attendance scenarios, traffic scenarios, etc.

In order to avoid the abnormality of the biological recognition, the biological recognition model is trained by introducing abundant and diverse biological characteristic samples at the present stage, so that the stability of the biological recognition model is improved, and the possibility of the abnormality in the biological recognition process is reduced. But the biometric terminal may also be abnormal due to hardware or external influence factors, etc. The detection of abnormal biometric terminals is difficult to realize due to the large difference of software, hardware, external influence factors and the like of different biometric terminals for biometric identification.

Disclosure of Invention

The embodiment of the application provides a method, a device, equipment and a storage medium for detecting abnormity of a biological identification terminal, which can realize the detection of the abnormal biological identification terminal.

In one aspect, an embodiment of the present application provides a method for detecting an abnormality of a biometric terminal, including: acquiring historical identification data of a target biological identification terminal, wherein the historical identification data comprises first biological characteristic data and a similarity parameter value of the first biological characteristic data and sample biological characteristic data in an identification database; obtaining a first distribution characteristic associated with first biological characteristic data under each similarity parameter value based on historical identification data; and determining that the target biological identification terminal is abnormal under the condition that the first distribution characteristic does not accord with any preset standard distribution condition, wherein the standard distribution condition is related to the distribution characteristics of the second biological characteristic data of the biological identification terminal which is not abnormal under each similarity parameter value.

In a second aspect, an embodiment of the present application provides a biometric terminal abnormality detection apparatus, including: the data acquisition module is used for acquiring historical identification data of the target biological identification terminal, wherein the historical identification data comprises first biological characteristic data and a similarity parameter value between the first biological characteristic data and sample biological characteristic data in an identification database; the first processing module is used for obtaining a first distribution characteristic associated with the first biological characteristic data under each similarity parameter value based on historical identification data; and the abnormality determination module is used for determining that the target biological identification terminal is abnormal under the condition that the first distribution characteristic does not accord with any preset standard distribution condition, wherein the standard distribution condition is related to the distribution characteristics of the biological identification terminal which is not abnormal and is associated with the second biological characteristic data under each similarity parameter value in the standard condition.

In a third aspect, an embodiment of the present application provides a biometric terminal abnormality detection apparatus, including: a processor and a memory storing computer program instructions; the processor, when executing the computer program instructions, implements the biometric terminal abnormality detection method of the first aspect.

In a fourth aspect, the present application provides a computer storage medium, on which computer program instructions are stored, and when executed by a processor, the computer program instructions implement the biometric terminal abnormality detection method in the first aspect.

According to the method, the device, the equipment and the storage medium for detecting the abnormity of the biological recognition terminal, the historical recognition data of the target biological recognition terminal is utilized to obtain the first distribution characteristic associated with the first biological characteristic data under the similarity parameter value. The standard distribution condition can be obtained by utilizing the distribution characteristics of the second biological characteristic data of the biological identification terminal without abnormality under the similarity parameter value in advance. If the target biometric terminal is not abnormal, the distribution of the target biometric terminal related to the first biometric data under each similarity parameter value should be similar to or close to the distribution of the second biometric data under each similarity parameter value of the biometric terminal without abnormality. Therefore, the standard distribution condition can be used as a basis for judging whether the target biological identification terminal is abnormal or not. And under the condition that the first distribution characteristic does not meet any preset standard distribution condition, the distribution condition of the target biological identification terminal related to the first biological characteristic data under each similarity parameter value is represented, and the difference with the distribution condition of the second biological characteristic of the biological identification terminal without abnormality under each similarity parameter value is larger, and the difference is caused by the abnormality of the target biological identification terminal. Therefore, whether the target biological identification terminal is abnormal or not can be determined according to whether the first distribution characteristics meet the standard distribution conditions or not, and therefore detection of the abnormal biological identification terminal is achieved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiments of the present application will be briefly described below, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic diagram of an architecture of a biometric system according to an embodiment of the present application;

fig. 2 is a flowchart of an anomaly detection method for a biometric terminal according to an embodiment of the present application;

fig. 3 is a schematic diagram of an example of a distribution curve of first biometric data under a similarity parameter value according to an embodiment of the present application;

fig. 4 is a schematic diagram of an example of a distribution curve of second biometric data under a similarity parameter value provided in an embodiment of the present application;

fig. 5 is a schematic diagram of another example of a distribution curve of first biometric data under a similarity parameter value according to an embodiment of the present application;

fig. 6 is a schematic diagram of another example of a distribution curve of first biometric data under a similarity parameter value provided in an embodiment of the present application;

fig. 7 is a schematic diagram illustrating a distribution curve of first biometric data under a similarity parameter value according to an embodiment of the present application;

fig. 8 is a schematic diagram of an example of a distribution of similarity parameter values between a user identification success and a user identification failure according to an embodiment of the present application;

fig. 9 is a flowchart of an abnormality detection method for a biometric terminal according to another embodiment of the present application;

fig. 10 is a flowchart of an abnormality detection method for a biometric terminal according to another embodiment of the present application;

fig. 11 is a schematic structural diagram of an abnormality detection apparatus for a biometric terminal according to an embodiment of the present application;

fig. 12 is a schematic structural diagram of an abnormality detection apparatus for a biometric terminal according to another embodiment of the present application;

fig. 13 is a schematic structural diagram of an abnormality detection device of a biometric terminal according to an embodiment of the present application.

Detailed Description

Features and exemplary embodiments of various aspects of the present application will be described in detail below, and in order to make objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended to be illustrative only and are not intended to be limiting. It will be apparent to one skilled in the art that the present application may be practiced without some of these specific details. The following description of the embodiments is merely intended to provide a better understanding of the present application by illustrating examples thereof.

The biometric technology is a technology for performing identity authentication by using biometric features, and may be applied to, for example, a transaction, attendance, passage, and the like, but is not limited thereto. Fig. 1 is a schematic diagram of an architecture of a biometric identification system according to an embodiment of the present disclosure. As shown in fig. 1, the biometric system may include a biometric terminal 11 and a biometric server 12. Each biometric server 12 may communicate with one biometric terminal 11 or with a plurality of biometric terminals 11, and is not limited thereto. The type of the biometric terminal 11 may be determined according to an application scenario and is not limited herein. For example, in a transaction scenario, the biometric terminal may be a face payment vending machine, an automated teller machine, or the like. For another example, in an attendance scene, the biometric terminal 11 may be a face recognition attendance machine or the like. For another example, in a traffic scenario, the biometric terminal 11 may identify a traffic gate or the like for a human face.

The biometric terminal 11 receives the biometric trigger request, and acquires the biometric characteristic of the user to be tested in response to the biometric trigger request. According to the biometrics characteristic, biometrics characteristic data is generated, and the biometrics characteristic data is uploaded to the biometrics identification server 12. The biometric server 12 has an identification database provided therein. The identification database includes sample biometric data, each sample biometric data corresponding to a user having an authority. The biometric server 12 calculates the similarity between the transmitted biometric data and the sample biometric data. If the similarity is higher than or equal to the threshold value for defining the identification success and failure, the biometric server 12 determines that the biometric identification of the user to be tested is successful, and considers that the user with the authority includes the user to be tested. If the similarity is lower than the threshold value for defining the success and failure of the identification, the biometric server 12 determines that the biometric identification of the user to be tested is failed, and considers that the user with the authority does not include the user to be tested.

In some cases, the biometric terminal 11 and the biometric server 12 may be integrated into the same device, which is not limited herein.

However, the biometric terminals may be attacked maliciously due to the great differences among the camera types, the image acquisition algorithms, the surrounding environment and the age and race of the user to be tested. These differences and malicious attacks may affect the biometric recognition algorithm, may confuse the recognition result of the biometric recognition algorithm, or may cause false recognition in which two different persons are recognized as the same person and missed recognition in which a user having a right is recognized as a user not having the right, thereby reducing the accuracy of the biometric recognition and causing an abnormality in the biometric recognition terminal. But such anomalies are difficult to detect at this stage.

The embodiment of the application provides a method, a device, equipment and a storage medium for detecting the abnormality of a biological identification terminal, which can detect whether the biological identification terminal is abnormal or not so as to take corresponding measures subsequently. The biometric terminal abnormality detection method in the embodiment of the present application may be applied to a biometric server or a biometric terminal and a biometric server integrated together, and is not limited herein.

Fig. 2 is a flowchart of an abnormality detection method for a biometric terminal according to an embodiment of the present disclosure. As shown in fig. 2, the biometric terminal abnormality detection method may include steps S201 to S203.

In step 201, historical identification data of the target biometric terminal is acquired.

The historical identification data may be data acquired by the target biometric terminal over a period of time. Specifically, the historical identification data may include the first biometric data, and a similarity parameter value of the first biometric data to the sample biometric data in the identification database. In the embodiment of the present application, the biometric data includes, but is not limited to, face feature data, fingerprint feature data, palm print feature data, iris feature data, and the like. The first biological characteristic data is data corresponding to biological characteristics acquired by the target biological identification terminal. One piece of biological characteristic data corresponds to the biological characteristics acquired by the target biological identification terminal at one time. For example, when the target biometric terminal performs three biometrics acquisition for the same user a, three first biometrics data are correspondingly generated.

The similarity parameter value is used for representing the similarity between the two. A higher value of the similarity parameter for both indicates a higher similarity between the both. In some examples, the similarity parameter value may be expressed in percentage.

The identification database includes at least one sample biometric data. Each sample biometric data corresponds to a user having rights associated with the biometric terminal. For example, in a payment scenario, each sample biometric data in the identification database corresponds to a registered user, and the registered user can perform biometric payment through the target biometric terminal. And when the similarity parameter value of certain first biological characteristic data and sample biological characteristic data is higher than or equal to the identification success threshold value, the user corresponding to the first biological characteristic data and the user corresponding to the sample biological characteristic data are considered to be the same user. And under the condition that the similarity parameter value of certain first biological characteristic data and sample biological characteristic data is lower than the identification success threshold value, the user corresponding to the first biological characteristic data and the user corresponding to the sample biological characteristic data are considered to be different users. In the case where the identification database includes two or more sample biometric data, the similarity parameter values of the first biometric data and each sample biometric data may be calculated, respectively.

In step 202, based on the historical identification data, a first distribution feature associated with the first biometric data at each similarity parameter value is obtained.

The first distribution characteristic is used for characterizing the distribution of the similarity between the first biological characteristic data and the biological characteristic data of the samples in the identification database. In particular, the first distribution characteristic may be used to characterize the distribution of the first biometric data under the similarity parameter value, and may also be used to characterize the distribution of the identification performed by using the first biometric data under the similarity parameter value, which is not limited herein.

In some examples, a distribution curve may be employed to represent a distribution of similarity between the first biometric data and the sample biometric data. For example, fig. 3 is a schematic diagram of an example of a distribution curve of first biological feature data under the similarity parameter value provided in the embodiment of the present application. As shown in fig. 3, the abscissa of the coordinate system of the distribution curve is the similarity parameter value, and the ordinate is the distribution quantity of the first biometric data. Any point on the distribution curve may represent the number of first biometric data corresponding to the similarity parameter value in the historical identification data of the target biometric terminal. As can be seen from fig. 3, the distribution curve can be divided into two parts according to the recognition success threshold, wherein the left side of the dashed line L1 is the distribution part of the first biometric data under each similarity parameter value lower than the recognition success threshold, and the right side of the dashed line L1 is the distribution part of the first biometric data under each similarity parameter value higher than or equal to the recognition success threshold.

In step 203, in the case that the first distribution characteristic does not meet any preset standard distribution condition, it is determined that an abnormality occurs in the target biometric terminal.

The standard distribution condition is related to the distribution characteristics of the second biological characteristic data of the biological identification terminal without abnormality under each similarity parameter value. The biological identification terminal without abnormality is a biological identification terminal which is normal in software, hardware, surrounding environment and the like and is not attacked by malicious attacks. The second biological characteristic data is biological characteristic data generated by the biological identification terminal without abnormality according to the collected biological characteristics. The distribution of the second biological characteristic data of the biological recognition terminal without abnormality has a certain rule under each similarity parameter value. And the similarity parameter value related to the abnormal biological identification terminal is the similarity parameter value of the second biological characteristic data and the sample biological characteristic data in the identification database.

In some examples, a distribution curve may be employed to represent a distribution of similarity between the second biometric data and the sample biometric data. Fig. 4 is a schematic diagram of an example of a distribution curve of second biometric data under the similarity parameter value provided in the embodiment of the present application. As shown in fig. 4, the abscissa of the coordinate system in which the distribution curve is located is the similarity parameter value, and the ordinate is the distribution quantity of the second biometric data. Any one point on the distribution curve may indicate the number of second biometric data corresponding to the similarity parameter value in the historical identification data of the biometric terminal in which no abnormality has occurred. As can be seen from fig. 4, the distribution curve may be divided into two parts according to the recognition success threshold, the left side of the dotted line L1 is a distribution part of the second biometric data at each similarity parameter value lower than the recognition success threshold, and the right side of the dotted line L1 is a distribution part of the second biometric data at each similarity parameter value higher than or equal to the recognition success threshold.

As can be seen from the distribution curve associated with the biometric terminal in which no abnormality has occurred shown in fig. 4, the distribution curve has peaks and valleys alternately appearing. For example, the distribution curve in fig. 4 includes a peak point P1, a peak point P2, a valley point D1, a valley point D2, and a valley point D3. The similarity parameter corresponding to the peak point and the similarity parameter corresponding to the valley point have a certain limit range. The variation trend of the distribution curve has a certain rule. In the embodiment of the present application, in addition to the normal valley point, when the value of the ordinate of the point in the distribution curve is smaller as the value of the minimum value of the abscissa in the distribution curve is closer, one point of the distribution curve in the region close to the minimum value of the abscissa may be regarded as the valley point. That is, in addition to the normal valley value, in the process of reducing the similarity parameter value to the minimum value, if the number of biometric data decreases as the similarity parameter value decreases, the number of biometric data corresponding to the similarity parameter value close to the minimum value may be taken as the valley value. Similarly, in addition to the normal valley point, in the case where the value of the ordinate of the point in the distribution curve is smaller as the value of the abscissa of the point in the distribution curve is closer to the maximum value of the abscissa, one point of the distribution curve in the region close to the maximum value of the abscissa may be regarded as the valley point. That is, in addition to the normal valley value, if the number of biometric data decreases as the similarity parameter value increases in the process of increasing the similarity parameter value to the maximum value, the number of biometric data corresponding to the similarity parameter value close to the maximum value may be taken as the valley value.

And obtaining the distribution characteristics according to the distribution of the second biological characteristic data of the biological identification terminal without abnormality under each similarity parameter value. Based on the distribution characteristics, standard distribution conditions may be set. The standard distribution condition may be used to distinguish between a biometric terminal in which an abnormality occurs and a biometric terminal in which no abnormality occurs. The first distribution characteristics accord with all standard distribution conditions, represent the distribution situation of the target biological identification terminal related to the first biological characteristic data under each similarity parameter value, have similar points or are close to the distribution situation of the second biological characteristics of the biological identification terminal without abnormality under each similarity parameter value, and the biological identification terminal without abnormality can be determined. The first distribution characteristic does not accord with any standard distribution condition, the distribution condition of the target biological identification terminal related to the first biological characteristic data under each similarity parameter value is represented, the difference between the distribution condition of the target biological identification terminal and the distribution condition of the biological identification terminal without abnormity under each similarity parameter value is larger, and the abnormity of the target biological identification terminal can be determined.

The standard distribution conditions may be one or more than two, and are not limited herein. Under the condition that the standard distribution condition is one, if the first distribution characteristic meets the standard distribution condition, determining that the target biological identification terminal is not abnormal; and if the first distribution characteristic does not meet the standard distribution condition, determining that the target biological identification terminal is abnormal. Under the condition that the standard distribution conditions are more than two, if the first distribution characteristics accord with all the standard distribution conditions, determining that the target biological identification terminal is not abnormal; if the first distribution characteristic does not meet at least one of the standard distribution conditions, it may be determined that the target biometric terminal is abnormal.

In the embodiment of the application, the historical identification data of the target biological identification terminal is used for obtaining the first distribution characteristic associated with the first biological characteristic data under the similarity parameter value. The standard distribution condition can be obtained by utilizing the distribution characteristics of the second biological characteristic data of the biological identification terminal without abnormality under the similarity parameter value in advance. If the target biometric terminal is not abnormal, the distribution of the target biometric terminal related to the first biometric data under each similarity parameter value should be similar to or close to the distribution of the second biometric data under each similarity parameter value of the biometric terminal without abnormality. Therefore, the standard distribution condition can be used as a basis for judging whether the target biological identification terminal is abnormal or not. And under the condition that the first distribution characteristic does not meet any preset standard distribution condition, the distribution condition of the target biological identification terminal related to the first biological characteristic data under each similarity parameter value is represented, and the difference with the distribution condition of the second biological characteristic of the biological identification terminal without abnormality under each similarity parameter value is larger, and the difference is caused by the abnormality of the target biological identification terminal. Therefore, whether the target biological identification terminal is abnormal or not can be determined according to whether the first distribution characteristics meet the standard distribution conditions or not, so that the abnormal biological identification terminal can be detected, and the safety of biological identification is improved.

The first distribution characteristic, the standard distribution condition corresponding to the first distribution characteristic, and how to specifically determine that the target biometric terminal is abnormal will be described below with several examples.

In a first example, the first distribution characteristic includes a similarity parameter value corresponding to the first peak. The first peak is a peak of the number of first biometric data corresponding to each similarity parameter value. The number of the first biological characteristic data is consistent with the identification times. For example, fig. 5 is a schematic diagram of another example of a distribution curve of the first biometric data under the similarity parameter value provided in the embodiment of the present application. As shown in fig. 5, the distribution curve includes a peak point PA1 and a peak point PA 2. The abscissa value of the similarity parameter value corresponding to the peak point PA1, i.e., the peak point PA1, is xa1, and the ordinate value of the first peak value corresponding to the peak point PA1, i.e., the peak point PA1, is ya 1. The first peak is ya1, which indicates that the number of first biometric data is ya1, i.e., the number of identifications is ya1, under the similarity parameter value xa 1. The abscissa value of the similarity parameter value corresponding to the peak point PA2, i.e., the peak point PA2, is xa2, and the ordinate value of the first peak value corresponding to the peak point PA2, i.e., the peak point PA2, is ya 2. The first peak is ya2, which indicates that the number of first biometric data is ya2, i.e., the number of identifications is ya2, under the similarity parameter value xa 2.

Correspondingly, the standard distribution condition includes that the absolute value of the difference between the similarity parameter value corresponding to the first peak value and the similarity parameter value corresponding to the standard peak value is smaller than a first preset threshold value. Under the abnormal conditions that the hardware defect of the target biological identification terminal has great influence on the collected biological characteristics, the defect of the biological characteristic collection algorithm of the target biological identification terminal has great influence on the collected biological characteristics, the surrounding environment of the target biological identification terminal has great influence on the collected biological characteristics, the target biological identification terminal is maliciously attacked and broken, and the like, the similarity parameter value corresponding to the first peak value of the target biological identification terminal is greatly changed compared with the similarity parameter value corresponding to the standard peak value. Therefore, whether the target biometric terminal is abnormal or not can be detected by using the similarity parameter value corresponding to the first peak value.

In some examples, in a case where the distribution curve of the first biometric data includes a plurality of peak points, for example, two peak points, a first peak of the first-occurring peak point and a standard peak of the first-occurring peak point may be compared, and a first peak of the second-occurring peak point and a standard peak of the second-occurring peak point may be compared, in order of the similarity parameter value from small to large.

The standard peak includes a peak of the number of second biometric data at each similarity parameter value. For example, as shown in fig. 4, the peak points corresponding to the standard peaks include P1 and P2. The abscissa value of the peak point P1, which is the similarity parameter value corresponding to the peak point P1, is x1, and the ordinate value of the peak point P1, which is the first peak value corresponding to the peak point P1, is y 1. The abscissa value of the peak point P2, which is the similarity parameter value corresponding to the peak point P2, is x2, and the ordinate value of the peak point P2, which is the first peak value corresponding to the peak point P2, is y 2. The first preset threshold may be set according to a working scenario and a working requirement, and is not limited herein. For ease of description, the first preset threshold is denoted as z 1.

According to fig. 4 and 5, if | xa1-x1| ≧ z1 and/or | xa 2-x 2| ≧ z1, it indicates that the difference between the similarity parameter value corresponding to the first peak and the similarity parameter value corresponding to the standard peak is too large, and it can be determined that the target biometric terminal is abnormal; if | xa1-x1| < z1 and | xa 2-x 2| < z1 indicate that the difference between the similarity parameter value corresponding to the first peak and the similarity parameter value corresponding to the standard peak is within the normal range, it may be determined that the abnormality has not occurred in the target biometric terminal.

In a second example, the first distribution characteristic includes a similarity parameter value corresponding to the first trough. The first bottom value is a bottom value of the number of the first biometric data corresponding to each similarity parameter value. For example, as shown in fig. 5, the distribution curve includes a valley point DB1, a valley point DB2, and a valley point DB 3. The abscissa value of the valley point DB1, which is the similarity parameter value corresponding to the valley point DB1, is xb1, and the ordinate value of the valley point DB1, which is the first valley value corresponding to the valley point DB1, is yb 1. The first bottom value is yb1, which indicates that the number of first biometric data under the similarity parameter value xb1 is yb1, i.e., the number of recognitions is yb 1. The abscissa value of the valley point DB2, which is the similarity parameter value corresponding to the valley point DB2, is xb2, and the ordinate value of the valley point DB2, which is the first valley value corresponding to the valley point DB2, is yb 2. The first bottom value is yb2, which indicates that the number of first biometric data under the similarity parameter value xb2 is yb2, i.e., the number of recognitions is yb 2. The abscissa value of the valley point DB3, which is the similarity parameter value corresponding to the valley point DB3, is xb3, and the ordinate value of the valley point DB3, which is the first valley value corresponding to the valley point DB3, is yb 3. The first bottom value is yb3, which indicates that the number of first biometric data under the similarity parameter value xb3 is yb3, i.e., the number of recognitions is yb 3.

Correspondingly, the standard distribution condition includes that the absolute value of the difference between the similarity parameter value corresponding to the first valley value and the similarity parameter value corresponding to the standard valley value is smaller than a second preset threshold. Under the abnormal conditions that the hardware defect of the target biological identification terminal has great influence on the collected biological characteristics, the defect of the biological characteristic collection algorithm of the target biological identification terminal has great influence on the collected biological characteristics, the surrounding environment of the target biological identification terminal has great influence on the collected biological characteristics, the target biological identification terminal is maliciously attacked and broken, and the like, the similarity parameter value corresponding to the first valley value of the target biological identification terminal is greatly changed compared with the similarity parameter value corresponding to the standard valley value. Therefore, whether the target biological identification terminal is abnormal or not can be detected by using the similarity parameter value corresponding to the first valley value.

In some examples, in a case where the distribution curve of the first biometric data includes a plurality of valley points, for example, three valley points, the first valley value of the first-occurring valley point and the standard valley value of the first-occurring valley point may be compared, the first valley value of the second-occurring valley point and the standard valley value of the second-occurring valley point may be compared, and the first valley value of the third-occurring valley point and the standard valley value of the third-occurring valley point may be compared, in order of the similarity parameter value from small to large.

The standard trough includes a trough in the number of second biometric data at each similarity parameter value. For example, as shown in fig. 4, the valley points corresponding to the standard valleys include D1, D2, and D3. The abscissa value of the valley point D1, which is the similarity parameter value corresponding to the valley point D1, is x3, and the ordinate value of the valley point D1, which is the first valley value corresponding to the valley point D1, is y 3. The abscissa value of the valley point D2, which is the similarity parameter value corresponding to the valley point D2, is x4, and the ordinate value of the valley point D2, which is the first valley value corresponding to the valley point D2, is y 4. The abscissa value of the valley point D3, which is the similarity parameter value corresponding to the valley point D3, is x5, and the ordinate value of the valley point D3, which is the first valley value corresponding to the valley point D3, is y 5. The second preset threshold may be set according to a working scenario and a working requirement, and is not limited herein. For ease of description, the second preset threshold is denoted as z 2.

According to fig. 4 and 5, if | xb1-x3| ≧ z2, | xb 2-x 4| ≧ z2 and/or | xb 2-x 5| ≧ z2, it indicates that the difference between the similarity parameter value corresponding to the first valley value and the similarity parameter value corresponding to the standard valley value is too large, and it can be determined that the target biometric terminal is abnormal; if | xb1-x3| < z2, | xb 2-x 4| < z2 and | xb 2-x 5| < z2 indicate that the difference between the similarity parameter value corresponding to the first valley and the similarity parameter value corresponding to the standard valley is within a normal range, it may be determined that the target biometric terminal is not abnormal.

In a third example, the first distribution profile includes a number of first biometric data corresponding to the first similarity parameter value. The first similarity parameter value is a similarity parameter value corresponding to the standard valley value. The standard bottom value is a bottom value of the number of the second biometric data corresponding to each similarity parameter value. For example, as shown in fig. 4, the valley points corresponding to the standard valley values include a valley point D1, a valley point D2, and a valley point D3. The first similarity parameter value corresponding to the valley point D1 is x3, the first similarity parameter value corresponding to the valley point D2 is x4, and the first similarity parameter value corresponding to the valley point D3 is x 5. For convenience of explanation, one valley point in the distribution curve shown in fig. 5 will be explained here, and other valley points in the distribution curve shown in fig. 5 may be detected in this manner. As shown in fig. 5, the number of the first biometric data corresponding to x4 is y 4'.

Correspondingly, the standard distribution condition includes that the difference value between the number of the first biological feature data corresponding to the first similarity parameter value and the standard valley value is smaller than a third preset threshold value. The target biological recognition terminal cannot acquire accurate biological characteristics due to hardware defects of the target biological recognition terminal, influences of the surrounding environment of the target biological recognition terminal and other abnormalities, and if a clear face image cannot be acquired, the number of first biological characteristic data corresponding to a smaller value of an original first similarity parameter value is greatly increased, that is, the number of recognition times corresponding to a smaller value of the original first similarity parameter value is greatly increased. Therefore, whether the target biological identification terminal is abnormal or not can be detected by utilizing the number of the first biological characteristic data corresponding to the first similarity parameter value.

The third preset threshold may be set according to a working scenario and a working requirement, and is not limited herein. For ease of description, the third preset threshold is denoted as z 3.

According to fig. 4 and 5, if y 4' -y4| ≧ z3, it is indicated that the number of the first biometric data corresponding to the first similarity parameter value is too different from the standard valley value, it is determined that the target biometric terminal is abnormal; if y 4' -y4 < z3, it is indicated that the difference between the number of the first biometric data corresponding to the first similarity parameter value and the standard valley value is within the normal range, and it is determined that the target biometric terminal is not abnormal. It should be noted that other valley points in the distribution curve shown in fig. 5 can also be detected in this manner, and are not described herein again.

In a fourth example, the first distribution characteristic comprises a sum of a number of occurrences of the first peak. The first peak is a peak of the number of first biometric data corresponding to each similarity parameter value. The sum of the occurrence times of the first peak is the number of peaks in the distribution curve of the first biological characteristic data under the similarity parameter value. When there are a plurality of peaks in the distribution curve of the first biometric data under the similarity parameter value, the number of the first biometric data corresponding to the plurality of peaks, that is, the first peak, may be equal or different, and is not limited herein. The equal first peak occurs twice, and the number of occurrences of the first peak is counted as two. For example, as shown in fig. 5, the distribution curve includes two peak points, i.e., the sum of the number of occurrences of the first peak is 2.

Correspondingly, the standard distribution condition comprises that the sum of the number of occurrences of the first peak is equal to 2. Under the condition that the target biological identification terminal is not abnormal, the distribution curve of the first biological characteristic data under the similarity parameter value comprises two peak points. Under the conditions that the target biological identification terminal hardware fails to collect biological characteristics, the target biological identification terminal cannot collect the biological characteristics due to the surrounding environment influence, and the target biological identification terminal is attacked maliciously, the corresponding peak points disappear. Therefore, whether or not an abnormality occurs in the target biometric terminal can be detected using the number of peaks, which is the sum of the number of occurrences of the first peak.

For example, if the number of peaks in the distribution curve shown in fig. 5 is equal to 2, that is, the number of occurrences of the first peak is equal to 2, and the standard distribution condition is met, it may be determined that the target biometric terminal is not abnormal.

For another example, fig. 6 is a schematic diagram of another example of a distribution curve of the first biological feature data under the similarity parameter value provided in the embodiment of the present application. As shown in fig. 6, under an abnormal condition that biometric features cannot be acquired, such as a failure of target biometric terminal hardware, the number of first biometric feature data corresponding to the similarity parameter value close to the minimum value is greatly increased, so that a part of peak points in the distribution curve disappears. The peak points in the distribution curve shown in fig. 6 include only the peak point PA3, that is, the number of peak points in the distribution curve shown in fig. 6 is less than 2, that is, the sum of the number of occurrences of the first peak is less than 2, and it may be determined that the target biometric terminal is abnormal.

For another example, fig. 7 is a schematic diagram of another example of a distribution curve of the first biological feature data under the similarity parameter value provided in the embodiment of the present application. As shown in fig. 7, in an abnormal situation that the target biometric terminal is attacked maliciously or the like, so that the first biometric data of the user different from the user corresponding to the sample biometric data is successfully identified, the number of the first biometric data corresponding to the similarity parameter value close to the maximum value is greatly increased, and a part of peak points in the distribution curve disappears. The peak points in the distribution curve shown in fig. 7 include only the peak point PA4, that is, the number of peak points in the distribution curve shown in fig. 7 is less than 2, that is, the sum of the number of occurrences of the first peak is less than 2, and it may be determined that the target biometric terminal is abnormal.

In a fifth example, the first distribution characteristic comprises a sum of a number of occurrences of the first trough. The first bottom value is a bottom value of the number of the first biometric data corresponding to each similarity parameter value. The sum of the occurrence times of the first valley values is the number of valley points in the distribution curve of the first biological characteristic data under the similarity parameter value. When there are a plurality of valleys in the distribution curve of the first biometric data under the similarity parameter value, the number of first biometric data corresponding to the plurality of valleys, that is, the first valleys, may be equal or different, and is not limited herein. The equal first valley value appears twice, and the statistics of the number of the appearance times of the first valley value is two times. For example, as shown in fig. 5, the distribution curve includes three valley points, i.e., the sum of the number of occurrences of the first valley is 3.

Correspondingly, the standard distribution condition includes that the sum of the number of occurrences of the first valley is equal to 3. In the case that the target biometric terminal is not abnormal, the distribution curve of the first biometric data under the similarity parameter value should include three valley points. Under the conditions that the target biological identification terminal hardware fails to collect biological characteristics, the target biological identification terminal is influenced by the surrounding environment and collects the biological characteristics with low precision, and the target biological identification terminal is attacked maliciously, the corresponding valley point disappears. Therefore, whether or not an abnormality occurs in the target biometric terminal can be detected using the number of valleys, which is the sum of the number of occurrences of the first valley.

For example, if the number of valleys in the distribution curve shown in fig. 5 is equal to 3, that is, the number of occurrences of the first valley is equal to 3, and the standard distribution condition is met, it may be determined that the target biometric terminal is not abnormal.

For another example, as shown in fig. 6, in the case of an abnormality that the biometric characteristic cannot be acquired, such as a failure of the target biometric terminal hardware, the number of first biometric data corresponding to the similarity parameter value close to the minimum value is greatly increased, so that a part of the valley points in the distribution curve disappears. The valley point in the distribution curve shown in fig. 6 includes only the valley point DB4 and the valley point DB5, i.e., the number of valley points in the curve of the distribution shown in fig. 6 is less than 3, i.e., the sum of the number of occurrences of the first valley value is less than 3, it may be determined that the abnormality occurs in the target biometric terminal.

For another example, as shown in fig. 7, in an abnormal situation where the target biometric terminal is attacked maliciously or the like, so that the first biometric data of the user different from the user corresponding to the sample biometric data is successfully identified, the number of the first biometric data corresponding to the similarity parameter value close to the maximum value is greatly increased, and a part of the valley points in the distribution curve disappears. The valley point in the distribution curve shown in fig. 7 includes only the valley point DB6 and the valley point DB7, i.e., the number of valley points in the distribution curve shown in fig. 7 is less than 3, i.e., the sum of the number of occurrences of the first valley value is less than 3, it may be determined that the abnormality occurs in the target biometric terminal.

In a sixth example, the first distribution characteristic comprises a normalized number of samples. The number of samples at the second similarity parameter value comprises a sum of the number of sample biometric data for which the similarity parameter value with each of the first biometric data is the second similarity parameter value. The second similarity parameter value may be any one of similarity parameter values below the recognition success threshold, and is not limited herein.

The target biological identification terminal obtains the first biological characteristic data and respectively calculates the similarity parameter values of the first biological characteristic data and the biological characteristic data of each sample in the identification database. Under the condition that the target biological identification terminal is not abnormal, the similarity parameter value of a first biological characteristic data and a sample biological characteristic data is larger than or equal to the identification success threshold value at most.

For example, fig. 8 is a schematic diagram of an example of a distribution of similarity parameter values between a user identification success and a user identification failure according to an embodiment of the present application. As shown in fig. 8, a thin solid line indicates a user whose identification is successful, and a thick solid line indicates a user whose identification is failed. The abscissa is the number of the sample biometric data, and the ordinate is the similarity parameter value of the first biometric data and the sample biometric data. Only one sample biometric data is available because the similarity parameter value with the first biometric data in each recognition is greater than the recognition success threshold. Therefore, the sum of the numbers of the sample biometric data having the similarity parameter value higher than or equal to the recognition success threshold value with respect to each of the first biometric data is small, and the distribution of the sum of the numbers of the sample biometric data having the similarity parameter value lower than the recognition success threshold value with respect to each of the first biometric data should be in agreement with the distribution of the second biometric data of the biometric terminal in which no abnormality has occurred at each of the similarity parameter values lower than the recognition success threshold value, on the whole. That is, the distribution of the number of samples should be consistent with the distribution of the second biometric data of the biometric identification terminal without abnormality in the overall trend at each similarity parameter value lower than the identification success threshold.

For comparison, the number of samples may be normalized so that the distribution range of the number of samples after the normalization process coincides with the distribution range of the number of second biometric data at each similarity parameter value lower than the recognition success threshold.

Correspondingly, the standard distribution condition includes that the absolute value of the difference between the number of the normalized samples and the number of the second biological characteristic data under the second similarity parameter value is smaller than a fourth preset threshold. The fourth preset threshold may be specifically set according to a working scenario and a working requirement, and is not limited herein. In the case of abnormality of the target biometric terminal, the distribution of the number of samples may be different from the distribution of the second biometric data of the biometric terminal without abnormality in the overall trend at each similarity parameter value lower than the recognition success threshold. Therefore, the normalized number of samples can be used to detect whether the target biometric terminal is abnormal.

The absolute value of the difference between the number of the normalized samples and the number of the second biological characteristic data under the second similarity parameter value is smaller than a fourth preset threshold, which indicates that the distribution of the number of the samples and the distribution of the second biological characteristic data under each similarity parameter value lower than the identification success threshold of the biological identification terminal without abnormality have smaller overall trend difference and are within an acceptable range. The absolute value of the difference between the number of the normalized samples and the number of the second biological characteristic data under the second similarity parameter value is greater than or equal to a fourth preset threshold, which indicates that the distribution of the number of the samples has a larger overall trend difference with the distribution of the second biological characteristic data of the biological identification terminal without abnormality under each similarity parameter value lower than the identification success threshold.

In a seventh example, the first distribution characteristic comprises a first fractional ratio. The first proportion is a ratio of the number of the first negative sample biometric data to the number of the first biometric data. The first negative sample biological characteristic data is the first biological characteristic data of which the similarity parameter value is smaller than the identification success threshold value. The similarity parameter value here refers to a similarity parameter value of the first biometric data and the sample biometric data.

Correspondingly, the standard distribution condition includes that the difference value of the first occupancy and the standard occupancy threshold is less than or equal to a fifth preset threshold. The standard proportion threshold value is an average value of first proportions of a plurality of target biological identification terminals, or a ratio of the number of second negative sample biological characteristic data of the biological identification terminal without abnormality to the number of second biological characteristic data in the standard condition. The second negative sample biological characteristic data is the second sample biological characteristic data with the similarity parameter value smaller than the identification success threshold value. The fifth preset threshold may be specifically set according to a working scenario and a working requirement, and is not limited herein.

Since the number of the first sample feature data that are failed to be identified in a single target biometric terminal is significantly too high, it indicates that the target biometric terminal is likely to be abnormal, such as being attacked or identified maliciously. The first duty and the standard duty threshold may be utilized to perform detection of whether an abnormality occurs in the target biometric terminal.

The first distribution characteristic may include, but is not limited to, one or more of a similarity parameter value corresponding to the first peak, a similarity parameter value corresponding to the first valley, the number of first biometric data corresponding to the first similarity parameter value, the sum of the occurrences of the first peak, the sum of the occurrences of the first valley, the number of samples after normalization processing, and the first percentage.

If the first distribution characteristics used for judging whether the target biological identification terminal is abnormal comprise a plurality of items and the corresponding standard distribution conditions comprise a plurality of items, when at least one of the first distribution characteristics does not meet the corresponding standard distribution condition, the target biological identification terminal can be determined to be abnormal.

Fig. 9 is a flowchart of an abnormality detection method for a biometric terminal according to another embodiment of the present application. Fig. 9 is different from fig. 2 in that the biometric terminal abnormality detection method shown in fig. 9 may further include steps S204 to S208.

In step S204, when the similarity parameter values of the N first biometric data continuously acquired by the target biometric terminal and the sample biometric data in the recognition database are all lower than the recognition success threshold value, and the similarity parameter values between the N first biometric data are higher than the similarity determination threshold value, it is determined that the target biometric terminal is abnormal, and the user corresponding to the N first biometric data is marked as an abnormal user.

The similarity parameter values of the N consecutive first biometric data and the sample biometric data in the identification database are all lower than the identification success threshold, and the similarity parameter values between the N first biometric data are higher than the similarity judgment threshold, which indicates that it is highly probable that the same user continuously tries to continue to identify under the condition of identification failure, and there is a possibility that the user performs malicious attack on the target biometric terminal. N is an integer greater than 1. On one hand, the target biological identification terminal can be determined to be abnormal; on the other hand, the user can be marked as an abnormal user, and the abnormal detection of the user operation behavior of the target biological recognition terminal can be realized.

The similarity determination threshold is a threshold for defining whether two pieces of biometric feature data indicate the same user, and may be set according to a work scenario and a work requirement, and is not limited herein.

In step S205, an abnormal user database is built by using the first biometric data of the abnormal user.

Namely, the abnormal user database comprises the first biological characteristic data of the abnormal user. In the case of obtaining the abnormal user by marking, the first biological characteristic data of the marked abnormal user can be stored into the abnormal user database, so that the abnormal user database can be used for identifying the abnormal user.

In step S206, a biometric request of the target biometric terminal is received.

Wherein the biometric request includes third biometric data.

In step S207, similarity parameter values of the third biometric data and the first biometric data of the abnormal user in the abnormal user database are calculated, respectively.

Before calculating the similarity parameter value between the third biometric data and the sample biometric data in the identification database, the similarity parameter value between the third biometric data and the first biometric data of the abnormal user in the abnormal user database is calculated respectively, so that when the abnormal user requests biometric identification, certain measures can be taken for the abnormal user to limit the biometric identification operation of the abnormal user.

In step S208, in a case where the similarity parameter value of the third biometric data to the first biometric data of the abnormal user in the abnormal user database is higher than the similarity determination threshold, the recognition success threshold for determining the success of biometric recognition is raised.

The similarity parameter value between the third biometric data and the first biometric data of the abnormal user in the abnormal user database is higher than the similarity determination threshold value, which indicates that the user corresponding to the third biometric data is an abnormal user.

In some examples, the recognition success threshold for the target biometric terminal may be increased. For example, the identification success threshold of the target biometric terminal is 80%; and under the condition that the similarity parameter value of the third biological characteristic data and the first biological characteristic data of the abnormal user in the abnormal user database is higher than the similarity judgment threshold value, increasing the identification success threshold value of the target biological identification terminal to 90%.

In other examples, the identification success threshold for the anomalous user may be increased. For example, the identification success threshold of the target biometric terminal is 80%; when the similarity parameter value of the third biometric data and the first biometric data of the abnormal user in the abnormal user database is higher than the similarity judgment threshold value, the recognition success threshold value of the recognition performed by the third biometric data and the sample biometric data in the recognition database is increased to 90%, but the recognition success threshold value of the recognition performed by the other third biometric data, the similarity parameter value of which and the first biometric data of the abnormal user in the abnormal user database is lower than or equal to the similarity judgment threshold value, and the sample biometric data in the recognition database is still maintained at 80%.

Since the abnormal users may have malicious attack users and normal users, the probability of successful identification of the malicious attack users can be reduced by improving the identification success threshold, and the normal users can be ensured to be successfully identified to a certain extent.

Fig. 10 is a flowchart of an abnormality detection method for a biometric terminal according to another embodiment of the present application. Fig. 10 is different from fig. 2 in that the biometric terminal abnormality detection method shown in fig. 10 may further include step S209 and/or step S210.

In step S209, when a biometric request for specifying a target biometric terminal in which an abnormality has occurred is received, the recognition success threshold for determining that biometric recognition is successful is raised.

By improving the identification success threshold of the target biological identification terminal with abnormal identification, the risk of biological identification abnormality can be reduced to a certain extent, and the safety of biological identification is improved.

In step S210, when a biometric request of an abnormality-related biometric terminal is received, a recognition success threshold for determining success of biometric recognition is raised.

The abnormality-related biometric terminal is a biometric terminal with the same attribute as that of the target biometric terminal with the abnormality. The attribute here is an attribute of the biometric terminal. For example, the attributes may include a terminal model, a terminal operator, and the like. When the biometric identification terminal is applied to a transaction scene, the attributes may further include a merchant to which the terminal belongs, an acquirer corresponding to terminal transaction, and the like. The attribute is not limited herein.

The possibility of the abnormity-associated biological identification terminal is higher than that of other biological identification terminals, and the safety of the biological identification of the abnormity-associated biological identification terminal is improved by improving the identification success threshold of the abnormity-associated biological identification terminal.

The embodiment of the application also provides a biological identification terminal abnormity detection device. Fig. 11 is a schematic structural diagram of an abnormality detection apparatus for a biometric terminal according to an embodiment of the present application. As shown in fig. 11, the biometric terminal abnormality detection apparatus 300 may include a data acquisition module 301, a first processing module 302, and an abnormality determination module 303.

The data obtaining module 301 may be configured to obtain historical identification data of the target biometric terminal, where the historical identification data includes first biometric data and a similarity parameter value between the first biometric data and sample biometric data in the identification database.

The first processing module 302 may be configured to obtain a first distribution feature associated with the first biometric data at each similarity parameter value based on the historical identification data.

The abnormality determination module 303 is configured to determine that the target biometric terminal is abnormal when the first distribution characteristic does not meet any preset standard distribution condition.

And the standard distribution condition is related to the distribution characteristics of the biological identification terminal without abnormality, which are related to the second biological characteristic data under the similarity parameter values in the standard condition.

In some examples, the first distribution characteristic includes a similarity parameter value corresponding to the first peak. The first peak is a peak of the number of first biometric data corresponding to each similarity parameter value.

Correspondingly, the standard distribution condition includes that the absolute value of the difference between the similarity parameter value corresponding to the first peak value and the similarity parameter value corresponding to the standard peak value is smaller than a first preset threshold value. The standard peak includes a peak of the number of second biometric data at each similarity parameter value.

In some examples, the first distribution characteristic includes a similarity parameter value corresponding to the first trough value. The first bottom value is a bottom value of the number of the first biometric data corresponding to each similarity parameter value.

Correspondingly, the standard distribution condition includes that the absolute value of the difference between the similarity parameter value corresponding to the first valley value and the similarity parameter value corresponding to the standard valley value is smaller than a second preset threshold. The standard trough includes a trough in the number of second biometric data at each similarity parameter value.

In some examples, the first distribution characteristic includes a number of first biometric data corresponding to the first similarity parameter value. The first similarity parameter value is a similarity parameter value corresponding to the standard valley value. The standard bottom value is a bottom value of the number of the second biometric data corresponding to each similarity parameter value.

Correspondingly, the standard distribution condition includes that the difference value between the number of the first biological feature data corresponding to the first similarity parameter value and the standard valley value is smaller than a third preset threshold value.

In some examples, the first distribution characteristic includes a sum of a number of occurrences of the first peak. The first peak is a peak of the number of first biometric data corresponding to each similarity parameter value.

Correspondingly, the standard distribution condition comprises that the sum of the number of occurrences of the first peak is equal to 2.

In some examples, the first distribution characteristic includes a sum of a number of occurrences of the first trough. The first bottom value is a bottom value of the number of the first biometric data corresponding to each similarity parameter value.

Correspondingly, the standard distribution condition includes that the sum of the number of occurrences of the first valley is equal to 3.

In some examples, the first distribution characteristic includes a normalized number of samples after the processing. The number of samples at the second similarity parameter value comprises a sum of the number of sample biometric data for which the similarity parameter value with each of the first biometric data is the second similarity parameter value. The second similarity parameter value is below an identification success threshold. The distribution range of the number of samples after the normalization process coincides with the distribution range of the number of second biometric data at each similarity parameter value lower than the recognition success threshold.

Correspondingly, the standard distribution condition includes that the absolute value of the difference between the number of the normalized samples and the number of the second biological characteristic data under the second similarity parameter value is smaller than a fourth preset threshold.

In some examples, the first distribution characteristic includes a first duty cycle. The first proportion is a ratio of the number of the first negative sample biometric data to the number of the first biometric data. The first negative sample biological characteristic data is the first biological characteristic data of which the similarity parameter value is smaller than the identification success threshold value.

The standard distribution condition includes that the difference between the first occupancy and the standard occupancy threshold is less than or equal to a fifth preset threshold. The standard proportion threshold is an average value of first proportions of the plurality of target biological identification terminals or a ratio of the number of second negative sample biological characteristic data of the biological identification terminal without abnormality to the number of second biological characteristic data in the standard condition. The second negative sample biological characteristic data is the second sample biological characteristic data with the similarity parameter value smaller than the identification success threshold value.

Fig. 12 is a schematic structural diagram of an abnormality detection apparatus for a biometric terminal according to another embodiment of the present application. Fig. 12 is different from fig. 11 in that the biometric terminal abnormality detection apparatus shown in fig. 12 may further include an abnormal user marking module 304, a database creation module 305, a reception module 306, a second processing module 307, and a threshold adjustment module 308.

The abnormal user labeling module 304 may be configured to, when similarity parameter values of N first biometric data continuously acquired by the target biometric terminal and sample biometric data in the recognition database are both lower than a recognition success threshold value, and the similarity parameter values between the N first biometric data are higher than a similarity determination threshold value, determine that the target biometric terminal is abnormal, and label a user corresponding to the N first biometric data as an abnormal user.

The database building module 305 may build the abnormal user database using the first biometric data of the abnormal user.

The receiving module 306 may be configured to receive a biometric request of the target biometric terminal, where the biometric request includes the third biometric data.

The second processing module 307 may be configured to calculate similarity parameter values between the third biometric data and the first biometric data of the abnormal user in the abnormal user database, respectively.

The threshold adjusting module 308 may be configured to increase the recognition success threshold for determining success of biometric recognition if the similarity parameter value between the third biometric data and the first biometric data of the abnormal user in the abnormal user database is higher than the similarity determination threshold.

In some examples, the threshold adjustment module 308 may be further configured to increase the identification success threshold for determining that biometric identification is successful if a biometric identification request of a target biometric identification terminal determined to be abnormal is received.

In some examples, the threshold adjustment module 308 may be further configured to increase an identification success threshold for determining success of biometric identification if a biometric request of an abnormally associated biometric terminal is received.

The abnormality-related biometric terminal is a biometric terminal with the same attribute as that of the target biometric terminal with the abnormality.

The embodiment of the application also provides a biological recognition terminal abnormity detection device. Fig. 13 is a schematic structural diagram of an abnormality detection device of a biometric terminal according to an embodiment of the present application. As shown in fig. 13, the biometric terminal abnormality detection device 400 includes a memory 401, a processor 402, and a computer program stored on the memory 401 and executable on the processor 402.

In one example, the processor 402 may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits of the embodiments of the present Application.

Memory 401 may include mass storage for data or instructions. By way of example, and not limitation, memory 401 may include a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, tape, or Universal Serial Bus (USB) Drive or a combination of two or more of these.

The Memory may include Read-Only Memory (ROM), Random Access Memory (RAM), magnetic disk storage media devices, optical storage media devices, flash Memory devices, electrical, optical, or other physical/tangible Memory storage devices. Thus, in general, the memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., a memory device) encoded with software comprising computer-executable instructions and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the biometric terminal anomaly detection methods according to the present application.

The processor 402 runs a computer program corresponding to the executable program code by reading the executable program code stored in the memory 401 for implementing the biometric terminal abnormality detection method in the above-described embodiment.

In one example, the biometric terminal abnormality detection device 400 may also include a communication interface 403 and a bus 404. As shown in fig. 13, the memory 401, the processor 402, and the communication interface 403 are connected by a bus 404 to complete communication therebetween.

The communication interface 403 is mainly used for implementing communication between modules, apparatuses, units and/or devices in the embodiments of the present application. Input devices and/or output devices may also be accessed through communication interface 403.

The bus 404 comprises hardware, software, or both that couple the components of the biometric terminal abnormality detection device 400 to one another. By way of example, and not limitation, Bus 404 may include an Accelerated Graphics Port (AGP) or other Graphics Bus, an Enhanced Industry Standard Architecture (EISA) Bus, a Front-Side Bus (FSB), a HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) Bus, an InfiniBand interconnect, a Low Pin Count (LPC) Bus, a memory Bus, a Micro Channel Architecture (MCA) Bus, a Peripheral Component Interconnect (PCI) Bus, a PCI-Express (PCI-X) Bus, a Serial Advanced Technology Attachment (SATA) Bus, a Video Electronics Standards Association Local Bus (VLB) Bus, or other suitable Bus, or a combination of two or more of these. Bus 404 may include one or more buses, where appropriate. Although specific buses are described and shown in the embodiments of the application, any suitable buses or interconnects are contemplated by the application.

An embodiment of the present application further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the method for detecting an abnormality of a biometric identification terminal in the foregoing embodiment can be implemented, and the same technical effect can be achieved. The computer-readable storage medium may include a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, which is not limited herein.

It should be clear that the embodiments in this specification are described in a progressive manner, and the same or similar parts in the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. For apparatus embodiments, device embodiments, computer-readable storage medium embodiments, reference may be made in the descriptive section to method embodiments. The present application is not limited to the particular steps and structures described above and shown in the drawings. Those skilled in the art may make various changes, modifications and additions or change the order between the steps after appreciating the spirit of the present application. Also, a detailed description of known process techniques is omitted herein for the sake of brevity.

Aspects of the present application are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions/acts specified in the flowchart and/or block diagram block or blocks. Such a processor may be, but is not limited to, a general purpose processor, a special purpose processor, an application specific processor, or a field programmable logic circuit. It will also be understood that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware for performing the specified functions or acts, or combinations of special purpose hardware and computer instructions.

It will be appreciated by persons skilled in the art that the above embodiments are illustrative and not restrictive. Different features which are present in different embodiments may be combined to advantage. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art upon studying the drawings, the specification, and the claims. In the claims, the term "comprising" does not exclude other means or steps; the word "a" or "an" does not exclude a plurality; the terms "first" and "second" are used to denote a name and not to denote any particular order. Any reference signs in the claims shall not be construed as limiting the scope. The functions of the various parts appearing in the claims may be implemented by a single hardware or software module. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

Claims

1. A biological identification terminal abnormity detection method is characterized by comprising the following steps:

acquiring historical identification data of a target biological identification terminal, wherein the historical identification data comprises first biological characteristic data and a similarity parameter value between the first biological characteristic data and sample biological characteristic data in an identification database;

obtaining a first distribution characteristic associated with the first biological characteristic data under each similarity parameter value based on the historical identification data;

and determining that the target biological identification terminal is abnormal under the condition that the first distribution characteristic does not meet any preset standard distribution condition, wherein the standard distribution condition is related to the distribution characteristics of second biological characteristic data of the biological identification terminal without abnormality under each similarity parameter value.

2. The method of claim 1, wherein the first distribution characteristic comprises a similarity parameter value corresponding to a first peak value, the first peak value being a peak value of the number of the first biometric data corresponding to each similarity parameter value,

the standard distribution condition comprises that the absolute value of the difference value between the similarity parameter value corresponding to the first peak value and the similarity parameter value corresponding to the standard peak value is smaller than a first preset threshold value, and the standard peak value comprises the peak value of the number of the second biological feature data under each similarity parameter value.

3. The method of claim 1, wherein the first distribution feature comprises similarity parameter values corresponding to first valleys, the first valleys being valleys of the number of the first biometric data corresponding to each similarity parameter value,

the standard distribution condition includes that an absolute value of a difference between a similarity parameter value corresponding to the first valley and a similarity parameter value corresponding to a standard valley is smaller than a second preset threshold, and the standard valley includes a valley of the number of second biometric data under each similarity parameter value.

4. The method of claim 1, wherein the first distribution feature comprises a number of the first biometric data corresponding to a first similarity parameter value, wherein the first similarity parameter value is a similarity parameter value corresponding to a standard valley value, and wherein the standard valley value is a valley value of a number of the second biometric data corresponding to each similarity parameter value,

the standard distribution condition comprises that the difference value between the number of the first biological feature data corresponding to the first similarity parameter value and the standard valley value is smaller than a third preset threshold value.

5. The method of claim 1, wherein the first distribution characteristic comprises a sum of a number of occurrences of a first peak, the first peak being a peak of a number of the first biometric data corresponding to each similarity parameter value,

the standard distribution condition includes that the sum of the number of occurrences of the first peak is equal to 2.

6. The method of claim 1, wherein the first distribution characteristic comprises a sum of a number of occurrences of the first trough, the first trough being a trough of a number of the first biometric data corresponding to each similarity parameter value,

the standard distribution condition includes that the sum of the number of occurrences of the first valley is equal to 3.

7. The method of claim 1, wherein the first distribution characteristic comprises a normalized number of the samples, wherein the number of the samples at a second similarity parameter value comprises a sum of a number of the sample biometric data for which a similarity parameter value of each of the first biometric data is the second similarity parameter value, wherein the second similarity parameter value is lower than an identification success threshold, and wherein a distribution range of the normalized number of the samples is consistent with a distribution range of the number of the second biometric data at each of the similarity parameter values lower than the identification success threshold;

the standard distribution condition comprises that the absolute value of the difference value between the number of the samples after normalization processing under the second similarity parameter value and the number of the second biological characteristic data is smaller than a fourth preset threshold value.

8. The method of claim 1, wherein the first distribution feature comprises a first fraction, the first fraction being a ratio of a number of first negative sample biometric data to a number of the first biometric data, the first negative sample biometric data being the first biometric data having a similarity parameter value less than an identification success threshold,

the standard distribution condition comprises that the difference value of the first ratio and a standard ratio threshold is smaller than or equal to a fifth preset threshold, the standard ratio threshold is the average value of the first ratios of the target biological identification terminals or the ratio of the number of second negative sample biological characteristic data of the biological identification terminals without abnormality in the standard condition to the number of the second biological characteristic data, and the second negative sample biological characteristic data is the second sample biological characteristic data with the similarity parameter value smaller than the identification success threshold.

9. The method of claim 1, further comprising:

and under the condition that the similarity parameter values of N pieces of first biological characteristic data continuously acquired by the target biological identification terminal and the sample biological characteristic data in the identification database are all lower than an identification success threshold value, and the similarity parameter values among the N pieces of first biological characteristic data are higher than a similarity judgment threshold value, determining that the target biological identification terminal is abnormal, marking the users corresponding to the N pieces of first biological characteristic data as abnormal users, wherein N is an integer greater than 1.

10. The method of claim 9, further comprising:

and establishing an abnormal user database by using the first biological characteristic data of the abnormal user.

11. The method of claim 10, further comprising:

receiving a biometric request of the target biometric terminal, wherein the biometric request comprises the third biometric characteristic data;

respectively calculating similarity parameter values of the third biological characteristic data and the first biological characteristic data of the abnormal user in the abnormal user database;

and in the case that the similarity parameter value of the third biological feature data and the first biological feature data of the abnormal user in the abnormal user database is higher than the similarity judgment threshold, increasing the identification success threshold for judging the success of biological identification.

12. The method of claim 1, further comprising:

and in the case of receiving a biometric identification request of the target biometric identification terminal for determining that the abnormality occurs, raising an identification success threshold for determining that the biometric identification is successful.

13. The method of claim 1, further comprising:

in the case where a biometric request of an abnormally associated biometric terminal is received, an identification success threshold for determining success of biometric identification is raised,

the abnormality associated biometric terminal is a biometric terminal having the same attribute as that of the target biometric terminal in which the abnormality occurs.

14. An abnormality detection device for a biometrics authentication terminal, characterized by comprising:

the data acquisition module is used for acquiring historical identification data of a target biological identification terminal, wherein the historical identification data comprises first biological characteristic data and a similarity parameter value of the first biological characteristic data and sample biological characteristic data in an identification database;

the first processing module is used for obtaining a first distribution characteristic associated with the first biological characteristic data under each similarity parameter value based on the historical identification data;

and the abnormality determination module is used for determining that the target biological identification terminal is abnormal under the condition that the first distribution characteristic does not meet any preset standard distribution condition, wherein the standard distribution condition is related to the distribution characteristics of the biological identification terminal which is not abnormal and is associated with the second biological characteristic data under each similarity parameter value in the standard condition.

15. An abnormality detection device for a biometrics identification terminal, characterized in that the device comprises: a processor and a memory storing computer program instructions;

the processor, when executing the computer program instructions, implements the biometric terminal abnormality detection method according to any one of claims 1 to 13.

16. A computer storage medium characterized in that the computer storage medium has stored thereon computer program instructions which, when executed by a processor, implement the biometric terminal abnormality detection method according to any one of claims 1 to 13.