CN106156727B - A kind of recognition methods and terminal of biological characteristic - Google Patents

Abstract

The embodiment of the present invention discloses a method for identifying a biometric feature and a terminal, which are used to speed up the identification of the biometric feature by the terminal. The method of the embodiment of the present invention includes: the terminal determines the target equal error rate and the target false recognition rate; the terminal reduces the dimension of the first original feature of the first sample according to the target equal error rate to obtain the first feature, and according to the target false recognition rate An original feature is dimensionally reduced to obtain a second feature, and the first sample is a sample pre-stored or collected by the terminal; the terminal stores the first feature and the second feature; the terminal obtains the biological image of the object to be identified to generate the second sample and extracts the first sample The second original feature of the two samples; the terminal reduces the dimension of the second original feature according to the target equal error rate to obtain the third feature, and reduces the dimension of the second original feature according to the target false recognition rate to obtain the fourth feature; the terminal combines the third feature with The first feature is compared and identified, and the fourth feature is compared and identified with the second feature to obtain an identification result.

Description

A biometric identification method and terminal

technical field

The present invention relates to the field of terminals, and in particular, to a biometric identification method and a terminal.

Background technique

Biometrics is a technology that uses human biometrics for identity authentication. Biometrics are unique (different from others) physiological characteristics or behaviors that can be measured or automatically identified and verified. They are divided into physiological characteristics and behavioral characteristics. The biometric system samples characteristic features, extracts their unique characteristics and performs identity authentication. As a means of identification, biometrics has unique advantages and has gradually become an international research hotspot in recent years. At present, the more mature and large-scale use methods are mainly fingerprint, iris, face, ear, palm print, palm vein, etc. The biometric identification technology is usually processed according to several steps of scanning, digital processing, analysis, feature extraction, storage, matching and classification.

At present, in order to obtain high reliability in biometric recognition, high-dimensional features are usually extracted as much as possible in the feature extraction stage. Local Binary Patterns (English full name: Local Binary Patterns, LBP for short) feature, if unified LBP (English full name: Unified LBP) is used, its features are 9x9x58; the features obtained by the feature extraction algorithm based on the block at key points will also be There are many, for example, 7 keypoints, and the unified LBP histogram feature obtained by the 4x4 block method at each keypoint is 7x4x4x58=6496.

However, the high-dimensional features are large in size and have many features, which in turn leads to complicated computing processes in the comparison and identification of the terminal, thereby reducing the speed at which the terminal recognizes biometrics.

SUMMARY OF THE INVENTION

The embodiments of the present invention provide a method and a terminal for identifying biometrics, which are used to speed up the identification of biometrics by the terminal.

In a first aspect, an embodiment of the present invention provides a method for identifying biological features, including:

The terminal determines the target equal error rate and the target false recognition rate; the terminal reduces the dimension of the first original feature of the first sample according to the target equal error rate to obtain the first feature, and reduces the first original feature according to the target false recognition rate. Dimension to obtain a second feature, the first sample is a sample pre-stored or collected by the terminal; the terminal stores the first feature and the second feature; the terminal obtains the biological image of the object to be identified to generate a second sample and Extract the second original feature of the second sample; the terminal reduces the dimension of the second original feature according to the target equal error rate to obtain the third feature, and reduces the dimension of the second original feature according to the target false recognition rate to obtain the fourth feature feature; the terminal compares and identifies the third feature with the first feature, and compares and identifies the fourth feature with the second feature to obtain an identification result.

In practical applications, the terminal reduces the dimension of the first original feature of the first sample to obtain the first feature according to the target equal error rate, which may be after the terminal reduces the dimension of the first original feature to obtain the second feature according to the target false recognition rate , the terminal then reduces the dimension of the second feature according to the target equal error rate to obtain the first feature, and the specific method is not limited here.

In a possible implementation manner, the target equal error rate is the minimum equal error rate within a first value range such that the first feature does not exceed the second value range, and the first value range is a minimum equal error rate of the target equal error rate. the value range, the second value range is the value range of the first feature;

The target false acceptance rate is within the third value range so that the second feature does not exceed the minimum false recognition rate of the fourth value range, the third value range is the value range of the target false recognition rate, and the fourth The value range is the value range of the second feature.

In another possible implementation manner, the terminal compares and identifies the third feature with the first feature, and compares and identifies the fourth feature with the second feature to obtain an identification result including: the terminal judging the third feature Whether the similarity between the feature and the first feature is less than the first threshold;

If the similarity between the third feature and the first feature is not less than the first threshold, the terminal determines whether the fourth feature and the second feature are less than the second threshold;

If the similarity between the fourth feature and the second feature is not less than the second threshold, the terminal determines that the second sample is the same as the first sample.

In another possible implementation manner, after the terminal determines whether the similarity between the third feature and the first feature is less than a first threshold, the method further includes: if the similarity between the third feature and the first feature is less than the the first threshold, the terminal determines that the second sample is different from the first sample.

In another possible implementation manner, after the terminal determines whether the fourth feature and the second feature are less than a second threshold, the method further includes: if the similarity between the fourth feature and the second feature is less than the second threshold, Then the terminal determines that the second sample is different from the first sample.

In another possible implementation manner, the terminal storing the first feature and the second feature includes:

The terminal quantizes the first feature according to the relationship to obtain the first quantized feature, and quantizes the second feature according to the relationship to obtain the second quantized feature;

The terminal stores the first quantitative feature and the second quantitative feature;

The terminal compares and identifies the third feature with the first feature and compares and identifies the fourth feature with the second feature to obtain an identification result including:

The terminal quantizes the third feature according to the relationship to obtain a third quantized feature, and quantizes the fourth feature according to the relationship to obtain a fourth quantized feature;

The terminal compares and identifies the third quantitative feature with the first quantitative feature, and compares and identifies the fourth quantitative feature with the second quantitative feature to obtain an identification result.

In practical applications, the terminal may also store a personal identification password (full English name: Personal Identification Number, abbreviation: PIN) corresponding to the first feature and the second feature.

In another possible implementation, the relation is:

Among them, the V is the feature value of the sample after dimensionality reduction, the _Vmin is the minimum feature value after the sample dimensionality reduction, the _Vmax is the maximum feature value after the sample dimensionality reduction, and the value of N is 255 or 65535.

In a second aspect, an embodiment of the present invention provides a terminal, including:

A determination module is used to determine the target equal error rate and the target false recognition rate;

The first dimension reduction module is used to reduce the dimension of the first original feature of the first sample to obtain the first feature according to the target equal error rate determined by the determination module, and use the target false recognition rate determined by the determination module to reduce the dimension. The first original feature is dimensionally reduced to obtain a second feature, and the first sample is a sample pre-stored or collected by the terminal;

a storage module for storing the first feature and the second feature obtained by reducing the dimension by the dimensionality reduction module;

an acquisition module, configured to acquire a biological image of the object to be identified to generate a second sample and extract the second original feature of the second sample;

The second dimension reduction module is configured to reduce the dimension of the second original feature acquired by the acquisition module to obtain a third feature according to the target equal error rate determined by the determination module, and according to the target false recognition rate determined by the determination module to The second original feature obtained by the obtaining module is dimensionally reduced to obtain a fourth feature;

an identification module, configured to compare and identify the third feature obtained by the second dimension reduction module and the first feature obtained by the first dimension reduction module, and obtain the second dimension reduction module The fourth feature of , and the second feature obtained by dimension reduction of the first dimensionality reduction module are compared and identified to obtain an identification result.

In a possible implementation manner, the target equal error rate is the minimum equal error rate within a first value range such that the first feature does not exceed the second value range, and the first value range is a minimum equal error rate of the target equal error rate. the value range, the second value range is the value range of the first feature;

The target false acceptance rate is within the third value range so that the second feature does not exceed the minimum false recognition rate of the fourth value range, the third value range is the value range of the target false recognition rate, and the fourth The value range is the value range of the second feature.

In another possible implementation manner, the identification module includes:

a first judging unit for judging whether the similarity between the third feature and the first feature is less than a first threshold;

a second judging unit, configured to judge whether the fourth feature and the second feature are less than the second threshold if the first judging unit judges that the similarity between the third feature and the first feature is not less than the first threshold;

The first identifying unit is configured to judge that the second sample is the same as the first sample if the second judgment judges that the similarity between the fourth feature and the second feature is not less than a second threshold.

In another possible implementation manner, the identification module further includes:

The second identifying unit is configured to judge that the second sample is different from the first sample if the first judging unit judges that the similarity between the third feature and the first feature is smaller than the first threshold.

In another possible implementation manner, the identification module further includes:

A third identifying unit, configured to judge, by the terminal, that the second sample is different from the first sample if the second judging unit judges that the similarity between the fourth feature and the second feature is less than a second threshold.

In another possible implementation manner, the storage module includes:

a first quantization unit, configured to quantify the first feature according to the relational formula to obtain the first quantized feature, and quantify the second feature according to the relational formula to obtain the second quantized feature;

a storage unit for storing the first quantization feature and the second quantization feature obtained by quantizing the quantization unit;

The identification module includes:

a second quantization unit, configured to quantify the third feature according to the relational expression to obtain a third quantitative characteristic, and quantify the fourth characteristic according to the relational expression to obtain a fourth quantitative characteristic;

The fourth identification unit is configured to compare and identify the third quantitative feature with the first quantitative feature, and compare and identify the fourth quantitative feature with the second quantitative feature to obtain an identification result.

In another possible implementation, the relation is:

Among them, the V is the feature value of the sample after dimensionality reduction, the _Vmin is the minimum feature value after the sample dimensionality reduction, the _Vmax is the maximum feature value after the sample dimensionality reduction, and the value of N is 255 or 65535.

In a third aspect, an embodiment of the present invention provides a terminal, including:

transceivers, processors, memories and buses;

The transceiver, the processor and the memory are connected through the bus;

The processor has the following functions: determining the target equal error rate and the target false recognition rate; reducing the dimension of the first original feature of the first sample to obtain the first feature according to the target equal error rate determined by the determining module, and determining The target false recognition rate determined by the module reduces the dimension of the first original feature to obtain a second feature, and the first sample is a sample pre-stored or collected by the terminal;

The memory has the following functions: storing the first feature and the second feature obtained by reducing the dimensionality of the dimensionality reduction module;

The transceiver has the following functions: obtaining a biological image of the object to be identified to generate a second sample and extracting second original features of the second sample;

The processor has the following functions: reducing the dimension of the second original feature acquired by the acquiring module to obtain a third feature according to the target equal error rate determined by the determining module, and applying the target false recognition rate determined by the determining module to the third feature The second original feature obtained by the acquisition module is dimensionally reduced to obtain a fourth feature; the third feature obtained by the dimensionality reduction of the second dimensionality reduction module is compared with the first feature obtained by the first dimensionality reduction module. The fourth feature obtained by the dimension reduction of the second dimension reduction module is compared with the second feature obtained by the dimension reduction of the first dimension reduction module to obtain a recognition result.

It can be seen from the above technical solutions that the embodiment of the present invention has the following advantages: the terminal reduces the dimension of the first original feature of the first sample according to the target equal error rate and the target false recognition rate, respectively, to obtain the first feature and the second feature, and After the terminal obtains the biological image of the object to be recognized to obtain the second sample and extracts the second original feature of the second sample, the second original feature is also dimensionally reduced according to the target equal error rate and the target false recognition rate to obtain The third feature and the fourth feature; when the second sample is compared and identified with the first sample, the third feature is compared with the first feature, and the fourth feature is compared with the second feature. The biometric features of one sample and the second sample are obtained, thereby reducing the complexity of the computing process of the terminal in the comparison and identification process, thereby speeding up the speed of the terminal identifying the biometrics.

Description of drawings

FIG. 1 is a schematic diagram of an embodiment of a biometric identification method in an embodiment of the present invention;

FIG. 2 is a schematic diagram of an embodiment of a terminal in an embodiment of the present invention;

FIG. 3 is a schematic diagram of another embodiment of a terminal in an embodiment of the present invention.

Detailed ways

The embodiments of the present invention provide a method and a terminal for identifying biometrics, which are used to speed up the identification of biometrics by the terminal.

In order to make those skilled in the art better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only Embodiments are part of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

The following is a detailed description of each of the specific embodiments. Please refer to FIG. 1. An embodiment of the present invention provides a method for identifying biological features, including:

101. The terminal determines the target equal error rate and the target false recognition rate.

The user pre-sets the value range of the high-dimensional original feature after dimensionality reduction, the value range of the equal error rate and the value range of the false recognition rate; The value of the feature after dimensionality reduction by the error rate does not exceed the minimum equal error rate of the pre-valued range. The minimum false recognition rate that does not exceed the pre-valued range is the target false recognition rate.

In this embodiment, taking the original feature dimension of the face as 6496 as an example, if the value range of the dimension-reduced feature according to the equal error rate is [10, 20], the value of the dimension-reduced feature according to the false recognition rate The range is [200, 400], and the value range of the equal error rate is [0.03, 0.05] and the value range of the false recognition rate [0.000001, 0.000003]. In practical applications, if the equal error rate is 0.05, The terminal can reduce the original feature dimension to 10 dimensions. When the equal error rate is 0.03, the terminal can reduce the original feature dimension to 20 dimensions, then the terminal selects 0.03 as the target equal error rate. When the false rate is 0.000001, the terminal can reduce the original feature dimension to 400 dimensions, and when the false recognition rate is 0.000003, the terminal can reduce the original feature to 200 dimensions, then the terminal selects 0.000001 as the target equal to Error rate.

102. The terminal reduces the dimension of the first original feature of the first sample according to the target equal error rate to obtain the first feature, and reduces the dimension of the first original feature according to the target false recognition rate to obtain the second feature.

The terminal acquires the first sample that needs to be stored or collected in advance, and extracts the first original feature of the first sample. The terminal reduces the dimension of the first original feature according to the determined target equal error rate to obtain the first feature, and reduces the dimension of the first original feature according to the determined target false recognition rate to obtain the second feature.

In practical applications, when the terminal extracts the first original features of the face, various methods can be used. For example, after the face is divided into 9x9 blocks to extract the LBP features and the histogram is calculated, the obtained features are 9x9x256 original LBP features. For Unified LBP, its features are 9x9x58; the feature extraction algorithm based on the block at key points will also obtain a lot of features, for example, 7 key points, and the unified LBP histogram feature obtained by the block method of 4x4 at each key point is 7x4x4x58=6496, and the extraction method of the original features of the sample is not limited here. In this embodiment, the original feature of the face is 6496 as an example, the first feature obtained by reducing the dimension of the face according to the target equal error rate and the target false recognition rate selected in step 101 is 20 dimensions, and the second feature is 400 dimensions .

103. The terminal stores the first feature and the second feature.

The terminal stores the dimension-reduced first feature and the second feature.

In practical applications, the terminal may also determine a PIN corresponding to the first feature and the second feature, and store the PIN with the first feature and the second feature at the same time, so that the The first feature and the second feature are read directly.

In practical applications, if the storage space of the terminal is not large, the terminal can also use the relational formula: Quantize the first feature and the second feature to obtain the first quantitative feature and the second quantitative feature. For example, the first feature is 20 dimensions, the value of one dimension is 0.5, the value of the other dimension is 2, and the value of the largest dimension is is 3, the value of the smallest dimension is 0.2, and the value of N is 255, the quantization values of the features obtained according to the relational quantization are as follows: the quantization value of 0.5 is 27, the quantization value of 2 is 163, and the quantization value of 3 is 255, the quantization value of 0.2 is 0, that is, the first feature can be represented by an unsigned 8-bit integer from 0 to 255, and these unsigned 8-bit integers can be represented by one byte, then store the first feature. When a quantized feature is used, the first quantized feature only occupies 20 bytes, which greatly reduces the occupied storage space. At the same time, in practical applications, 8-bit signed integers from -128 to 127, 16-bit unsigned integers from 0 to 65535, or 16-bit signed integers from -32768 to 32767 can also be used to represent features during quantization. Do limit.

104. The terminal acquires the biological image of the object to be identified, generates a second sample, and extracts a second original feature of the second sample.

During use, the terminal acquires a biological image of an object to be recognized, such as a human face, takes the biological image as a second sample to be recognized after acquiring the biological image, and extracts the second original feature of the biological image.

In practical applications, the biometric image of the object to be recognized by the terminal may be fingerprint, iris, face, ear, palm print, palm vein, etc. At the same time, the method for the terminal to acquire the biometric image of the object to be recognized may be through a camera, infrared scanning Etc., the terminal extracts the second original feature by extracting the LBP feature and calculating the histogram or obtaining the feature based on the feature extraction algorithm of the key point processing block, which is not specifically limited here.

105. The terminal reduces the dimension of the second original feature according to the target equal error rate to obtain the third feature, and reduces the dimension of the second original feature according to the target false recognition rate to obtain the fourth feature.

The terminal performs dimensionality reduction on the second original feature according to the determined target equal error rate to obtain a third feature, and performs dimensionality reduction on the second original feature according to the determined target false recognition rate to obtain a fourth feature.

In practical applications, if the terminal quantifies the first and second features of the pre-stored or collected first sample when the storage space is small, the terminal also needs to store the first and second features of the second sample. The third feature and the fourth feature are quantized in the same way to obtain quantized features.

106. The terminal compares and identifies the third feature with the first feature, and compares and identifies the fourth feature with the second feature to obtain an identification result.

The terminal sets a first threshold for the similarity between the third feature and the first feature, and sets a second threshold for the similarity between the fourth feature and the second feature, and then the terminal determines that the third feature is the same as the second feature. Whether the similarity of the first feature is less than the first threshold; if the similarity of the third feature and the first feature is not less than the first threshold, the terminal determines whether the fourth feature and the second feature are less than the second feature Threshold; if the similarity between the fourth feature and the second feature is not less than the second threshold, the terminal determines that the second sample is the same as the first sample. If the similarity between the third feature and the first feature is less than the first threshold, the terminal determines that the second sample is not the same as the first sample, and does not need to compare and identify the fourth feature and the second feature If the similarity between the third feature and the first feature is not less than the first threshold and the similarity between the fourth feature and the second feature is less than the second threshold, the terminal also judges the The second sample is not the same as the first sample.

For example, when the first threshold is 0.8, the second threshold is 0.9, the similarity between the third feature and the first feature is 0.85, and the similarity between the fourth feature and the second feature is 0.9, the terminal determines that the second sample is the same as the first It is the same person, if the similarity between the third feature and the first feature is 0.79, the terminal determines that the second sample and the first sample are not the same person or the similarity between the third feature and the first feature is 0.85, then The similarity between the fourth feature and the second feature is 0.89, and the terminal also determines that the second sample and the first sample are not the same person.

In practical applications, if the terminal quantifies the first feature and the second feature of the pre-stored first sample when the storage space is small, then the terminal needs to quantify the first and second features of the first sample before performing comparison and identification. The third quantitative feature and the first quantitative feature and the fourth quantitative feature and the second quantitative feature are identified by the same comparison and identification method described above.

For ease of understanding, the biometric identification method in this embodiment is described in detail below with an actual application scenario. In this embodiment, terminals include but are not limited to mobile phones, card punching machines, etc. The terminal takes a punching card machine as an example.

If the punch card machine adopts a one-to-many identification method, it is assumed that there are three employees of the company, A, B, and C. The punch card machine needs to pre-store sample A, sample B, and sample C, where the samples are all faces. The original features of sample A, sample B and sample C are all 6496-dimensional, then the punch card machine performs dimension reduction on sample A, sample B, and sample C respectively according to the equal error rate of 0.03 to obtain the feature set (20A, 20B, 20C); Then, according to the false recognition rate of 0.000001, the sample A, sample B, and sample C are respectively dimensionally reduced to obtain a feature set (400A, 400B, 400C). The punch card machine then stores the feature set (20A, 20B, 20C) and the feature set (400A, 400B, 400C) respectively, that is, the feature set (20A, 20B, 20C) occupies 3*20 bytes, and the feature set (400A, 400B, 400C) occupies 3*400 bytes. If employee A needs to punch in, the punch-in machine obtains the face image of employee A through the camera as a recognition sample and extracts the original features, and then uses the same method to reduce the dimension and quantify the original features of employee A to obtain features 20a and 400a. The punch card machine loads the stored feature sets (20A, 20B, 20C) into the memory before comparing and identifying, and then compares 20a with 20A, 20B, and 20C, respectively, and obtains the following results: the similarity between 20a and 20A is 0.85, The similarity between 20a and 20B is 0.8, and the similarity between 20a and 20C is 0.75. The condition for the punch card machine to judge that it is the same person is that the similarity is not less than 0.8. At this time, the first quantitative feature whose similarity is not less than 0.8 is (20A , 20B); the punch card machine then loads the stored feature set (400A, 400B) into the memory, and then compares 400a with 400A and 400B respectively to obtain the following results: the similarity between 400a and 400A is 0.95, and the similarity between 400a and 400B is 0.95. is 0.5, and the condition for the punch card machine to judge that it is the same person is that the similarity is not less than 0.9. Since only the characteristics of sample A and the face image sample of employee A meet the requirements, the punch card machine considers that sample A is the same person as employee A and displays it. In practical application, if the punching machine compares 400a with 400A and 400B respectively, the following results are obtained: the similarity between 400a and 400A is 0.95, and the similarity between 400a and 400B is 0.9. The condition for the punching machine to judge that it is the same person is The similarity is not less than 0.9. Then the punch card machine can judge that both sample A and sample B are the same person as employee A and display it; or it can only consider that sample A with a similarity of 0.95 is the same person as employee A and display it, that is, only select the one with the highest similarity. The sample is the final result; or the two similarities can be compared to the third threshold again, where the third threshold is 0.95, then the punch card machine finally considers that sample A and employee A are the same person and displays it.

At the same time, the punch card machine can also adopt a one-to-one verification method. When the punch card machine stores the characteristics of sample A (20A, 400A), it will determine a PIN corresponding to the characteristics of sample A and save it to the memory of the punch card machine. middle. When employee A needs to punch in, enter the PIN, and the punch card machine reads and enters the characteristics corresponding to the PIN from the memory, that is, (20A, 400A) is loaded into the memory, and at the same time collects the face image of employee A, extracts the features and reduces the dimension to get (20a, 400a); the punching machine compares (20A, 20a), if the similarity is less than the first threshold of 0.8, the punching fails; if the similarity is not less than 0.8, the punching machine is further compared (400A, 400a), If the similarity is not less than the second threshold of 0.9, the check-in succeeds, and if the similarity is less than 0.9, the check-in fails.

In this embodiment, the terminal performs dimensionality reduction on the first original feature of the first sample according to the target equal error rate and the target false recognition rate, respectively, to obtain the first feature and the second feature and store it; when the terminal obtains the biological image of the object to be recognized After the second sample is obtained and the second original feature of the second sample is extracted, the second original feature is also dimensionally reduced according to the target equal error rate and the target false recognition rate to obtain the third feature and the fourth feature; when the second sample In the process of comparing and identifying with the first sample, the third feature is compared with the first feature, and the fourth feature is compared with the second feature. The complexity of the calculation process of the terminal in the comparison and identification process is reduced, thereby accelerating the speed of the terminal to identify the biometrics. At the same time, the first feature and the second feature can be quantized and stored, which can effectively save storage space.

The biometric identification method in the embodiment of the present invention is described above, and the terminal in the embodiment of the present invention is described below. Please refer to FIG. 2. An example of the terminal in the embodiment of the present invention includes:

A determination module 201, used for determining the target equal error rate and the target false recognition rate;

The first dimension reduction module 202 is used to reduce the dimension of the first original feature of the first sample to obtain the first feature according to the target equal error rate determined by the determination module, and according to the target false recognition rate determined by the determination module The first original feature is dimensionally reduced to obtain a second feature, and the first sample is a sample pre-stored or collected by the terminal;

A storage module 203, configured to store the first feature and the second feature obtained by reducing the dimension by the dimensionality reduction module;

an acquisition module 204, configured to acquire a biological image of the object to be identified, generate a second sample, and extract a second original feature of the second sample;

The second dimension reduction module 205 is configured to reduce the dimension of the second original feature acquired by the acquisition module to obtain a third feature according to the target equal error rate determined by the determination module, and to obtain a third feature according to the target false recognition rate determined by the determination module The second original feature obtained by the obtaining module is dimensionally reduced to obtain a fourth feature;

The identification module 206 is used to compare and identify the third feature obtained by the second dimension reduction module and the first feature obtained by the first dimension reduction module and reduce the dimension by the second dimension reduction module. The obtained fourth feature is compared and identified with the second feature obtained by the first dimensionality reduction module to obtain an identification result.

Optionally, in this embodiment, the target equal error rate is the minimum equal error rate within the first value range such that the first feature does not exceed the second value range, and the first value range is the target equal error rate The value range of , the second value range is the value range of the first feature;

The target false acceptance rate is within the third value range so that the second feature does not exceed the minimum false recognition rate of the fourth value range, the third value range is the value range of the target false recognition rate, and the fourth The value range is the value range of the second feature.

Optionally, the identification module 206 includes:

a first judging unit for judging whether the similarity between the third feature and the first feature is less than a first threshold;

a second judging unit, configured to judge whether the fourth feature and the second feature are less than the second threshold if the first judging unit judges that the similarity between the third feature and the first feature is not less than the first threshold;

The first identifying unit is configured to judge that the second sample is the same as the first sample if the second judgment judges that the similarity between the fourth feature and the second feature is not less than a second threshold.

Optionally, the identification module 206 further includes:

The second identifying unit is configured to judge that the second sample is different from the first sample if the first judging unit judges that the similarity between the third feature and the first feature is smaller than the first threshold.

Optionally, the identification module 206 further includes:

A third identifying unit, configured to judge, by the terminal, that the second sample is different from the first sample if the second judging unit judges that the similarity between the fourth feature and the second feature is less than a second threshold.

Optionally, the storage module 204 includes:

a first quantization unit, configured to quantify the first feature according to the relational formula to obtain the first quantized feature, and quantify the second feature according to the relational formula to obtain the second quantized feature;

a storage unit for storing the first quantization feature and the second quantization feature obtained by quantizing the quantization unit;

The identification module 206 includes:

a second quantization unit, configured to quantify the third feature according to the relational expression to obtain a third quantitative characteristic, and quantify the fourth characteristic according to the relational expression to obtain a fourth quantitative characteristic;

The fourth identification unit is configured to compare and identify the third quantitative feature with the first quantitative feature, and compare and identify the fourth quantitative feature with the second quantitative feature to obtain an identification result.

Optionally, the relation is:

Among them, the V is the feature value of the sample after dimensionality reduction, the _Vmin is the minimum feature value after the sample dimensionality reduction, the _Vmax is the maximum feature value after the sample dimensionality reduction, and the value of N is 255 or 65535.

In this embodiment, the first dimensionality reduction module 202 performs dimensionality reduction on the first original feature of the first sample according to the target equal error rate and the target false recognition rate determined by the determining module 201, respectively, to obtain the first feature and the second feature, and store the first feature and the second feature. The storage module 203 performs storage; when the acquisition module 204 acquires the biological image to obtain the second sample and extracts the second original feature of the second sample, the second dimension reduction module 205 also performs the second dimensionality reduction according to the target equal error rate and the target false recognition rate respectively. Dimensionality reduction is performed on the two original features to obtain the third feature and the fourth feature; when the second sample is compared and identified with the first sample, the identification module 206 compares the third feature with the first feature, and the fourth feature and The second feature is compared, because the biometric features of the first sample and the second sample are reduced, thereby reducing the complexity of the terminal in the process of comparing and identifying, thereby speeding up the speed of the terminal identifying the biometrics.

Specifically, referring to FIG. 3, another embodiment of the terminal in the embodiment of the present invention includes:

transceiver 301, processor 302, bus 303, memory 304;

The transceiver 301, the processor 302 and the memory 304 are connected through the bus 303;

The bus 303 may be a peripheral component interconnect (PCI for short) bus or an extended industry standard architecture (extended industry standard architecture, EISA for short) bus or the like. The bus can be divided into address bus, data bus, control bus and so on. For ease of presentation, only one thick line is used in FIG. 3, but it does not mean that there is only one bus or one type of bus.

The processor 302 may be a central processing unit (CPU for short), a network processor (NP for short), or a combination of CPU and NP.

The processor 302 may further include hardware chips. The above-mentioned hardware chip may be an application-specific integrated circuit (ASIC for short), a programmable logic device (PLD for short) or a combination thereof. The above-mentioned PLD can be a complex programmable logic device (CPLD for short), a field-programmable gate array (FPGA for short), a general array logic (GAL for short) or any of the above. combination.

The memory 304 may include volatile memory (volatile memory), such as random-access memory (random-access memory, RAM for short); the memory may also include non-volatile memory (non-volatile memory), such as flash memory (flash memory) memory), a hard disk drive (HDD for short) or a solid-state drive (SSD for short); the memory 304 may also include a combination of the above-mentioned types of memory.

Optionally, the memory 304 may also be used to store program instructions, and the processor 302 may call the program instructions stored in the memory 304 to execute one or more steps in the embodiment shown in FIG. 1 , or an optional implementation manner therein. , to realize the function of terminal behavior in the above method.

The processor 302 has the following functions: determine the target equal error rate and the target false recognition rate; reduce the dimension of the first original feature of the first sample to obtain the first feature according to the target equal error rate determined by the determining module, and obtain the first feature according to the target equal error rate determined by the determining module The target false recognition rate determined by the determining module reduces the dimension of the first original feature to obtain a second feature, and the first sample is a sample pre-stored or collected by the terminal;

The memory 304 has the following functions: storing the first feature and the second feature obtained by reducing the dimension by the dimensionality reduction module;

The transceiver 301 has the following functions: acquiring a biological image of an object to be identified to generate a second sample and extracting a second original feature of the second sample;

The processor 302 has the following functions: reducing the dimension of the second original feature obtained by the obtaining module to obtain a third feature according to the target equal error rate determined by the determining module, and obtaining a third feature according to the target false recognition rate determined by the determining module The second original feature obtained by the acquisition module is dimensionally reduced to obtain a fourth feature; the third feature obtained by the dimensionality reduction of the second dimensionality reduction module is compared with the first feature obtained by the dimensionality reduction of the first dimensionality reduction module The recognition result is obtained by comparing and identifying the fourth feature obtained by the second dimension reduction module and the second feature obtained by the first dimension reduction module.

Optionally, the target equal error rate is the minimum equal error rate within the first value range such that the first feature does not exceed the second value range, and the first value range is the value range of the target equal error rate , the second value range is the value range of the first feature;

The target false acceptance rate is within the third value range so that the second feature does not exceed the minimum false recognition rate of the fourth value range, the third value range is the value range of the target false recognition rate, and the fourth The value range is the value range of the second feature.

Optionally, the processor 302 further has the following functions: judging whether the similarity between the third feature and the first feature is less than a first threshold; if the first judging unit judges the third feature and the first feature If the similarity of the fourth feature and the second feature is not less than the first threshold, it is determined whether the fourth feature and the second feature are less than the second threshold; if the second judgment determines that the similarity between the fourth feature and the second feature is not less than the second threshold , then it is judged that the second sample is the same as the first sample.

Optionally, the processor 302 further has the following functions: if the first judgment unit judges that the similarity between the third feature and the first feature is less than the first threshold, then judge the second sample and the first The samples are not the same.

Optionally, the processor 302 further has the following functions: if the second judging unit judges that the similarity between the fourth feature and the second feature is less than a second threshold, the terminal judges that the second sample is the same as the second sample. A sample is not the same.

Optionally, the memory 304 further has the following functions: quantizing the first feature according to the relationship to obtain the first quantized feature, and quantizing the second feature according to the relationship to obtain the second quantized feature; the quantization The first quantization feature and the second quantization feature obtained by unit quantization are stored;

The processor 302 also has the following functions: quantizing the third feature according to the relationship to obtain a third quantized feature, and quantizing the fourth feature according to the relationship to obtain a fourth quantized feature; quantizing the third feature The feature is compared and identified with the first quantitative feature, and the fourth quantitative feature is compared and identified with the second quantitative feature to obtain an identification result.

Optionally, the relation is:

Among them, the V is the feature value of the sample after dimensionality reduction, the _Vmin is the minimum feature value after the sample dimensionality reduction, the _Vmax is the maximum feature value after the sample dimensionality reduction, and the value of N is 255 or 65535.

In this embodiment, the processor 302 performs dimensionality reduction on the first original feature of the first sample according to the target equal error rate and the target false recognition rate, respectively, to obtain the first feature and the second feature, which are stored in the memory 304; when the transceiver 301 After acquiring the biological image of the object to be recognized to obtain the second sample and extracting the second original feature of the second sample, the processor 302 also performs dimensionality reduction on the second original feature according to the target equal error rate and the target false recognition rate to obtain the third sample. feature and the fourth feature; when the second sample is compared and identified with the first sample, the processor 302 compares the third feature with the first feature, and the fourth feature with the second feature. The biometric features of the first sample and the second sample are obtained, thereby reducing the complexity of the computing process of the terminal during the comparison and identification process, thereby speeding up the speed of the terminal identifying the biometric features.

Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system, device and unit described above may refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

In the several embodiments provided in this application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented. On the other hand, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in electrical, mechanical or other forms.

The units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated units may be implemented in the form of hardware, or may be implemented in the form of software functional units.

The integrated unit, if implemented in the form of a software functional unit and sold or used as an independent product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium , including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: U disk, removable hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes.

As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand: The technical solutions described in the embodiments are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (13)

1. a kind of identification method of biological feature, is characterized in that, comprises: The terminal determines the target equal error rate and the target false recognition rate, the target equal error rate is the minimum equal error rate within the first value range so that the first feature does not exceed the second value range, and the first value range is The value range of the target equal error rate, the second value range is the value range of the first feature; the target recognition rate is within the third value range so that the second feature does not exceed the fourth value range The minimum false recognition rate of the value range, the third value range is the value range of the target false recognition rate, and the fourth value range is the value range of the second feature; The terminal reduces the dimension of the first original feature of the first sample according to the target equal error rate to obtain the first feature, and reduces the dimension of the first original feature according to the target false recognition rate to obtain the second feature. A sample is a sample pre-stored or collected by the terminal; The terminal stores the first characteristic and the second characteristic; The terminal obtains the biological image of the object to be identified to generate a second sample and extracts the second original feature of the second sample; The terminal reduces the dimension of the second original feature according to the target equal error rate to obtain a third feature, and reduces the dimension of the second original feature according to the target false recognition rate to obtain a fourth feature; The terminal compares and identifies the third feature with the first feature, and compares and identifies the fourth feature with the second feature to obtain an identification result. 2. The method according to claim 1, wherein the terminal compares and identifies the third feature with the first feature and compares the fourth feature with the second feature The identification results obtained include: The terminal determines whether the similarity between the third feature and the first feature is less than a first threshold; If the similarity between the third feature and the first feature is not less than the first threshold, the terminal determines whether the fourth feature and the second feature are less than the second threshold; If the similarity between the fourth feature and the second feature is not less than a second threshold, the terminal determines that the second sample is the same as the first sample. 3. The method according to claim 2, wherein after the terminal determines whether the similarity between the third feature and the first feature is less than a first threshold, the method further comprises: If the similarity between the third feature and the first feature is less than the first threshold, the terminal determines that the second sample is different from the first sample. 4. The method according to claim 2, wherein after the terminal determines whether the fourth characteristic and the second characteristic are less than a second threshold, the method further comprises: If the similarity between the fourth feature and the second feature is less than a second threshold, the terminal determines that the second sample is different from the first sample. 5. The method according to any one of claims 1 to 4, wherein the storing, by the terminal, the first characteristic and the second characteristic comprises: The terminal quantizes the first feature according to the relationship to obtain the first quantized feature, and quantizes the second feature according to the relationship to obtain the second quantized feature; The terminal stores the first quantization feature and the second quantization feature; The terminal compares and identifies the third feature with the first feature and compares and identifies the fourth feature with the second feature to obtain an identification result including: The terminal quantizes the third feature according to the relationship to obtain a third quantized feature, and quantizes the fourth feature according to the relationship to obtain a fourth quantized feature; The terminal compares and identifies the third quantitative feature with the first quantitative feature, and compares and identifies the fourth quantitative feature with the second quantitative feature to obtain an identification result. 6. The method according to claim 5, wherein the relational expression is: The V is the feature value of the sample after dimensionality reduction, the _Vmin is the minimum feature value after the sample dimensionality reduction, the _Vmax is the maximum feature value after the sample dimensionality reduction, and the value of N is 255 or 65535. 7. A terminal, characterized in that, comprising: The determining module is used to determine the target equal error rate and the target false recognition rate, the target equal error rate is the minimum equal error rate within the first value range so that the first feature does not exceed the second value range, the first The value range is the value range of the target equal error rate, the second value range is the value range of the first feature; the target recognition rate is within the third value range so that the second feature The minimum false acceptance rate that does not exceed the fourth value range, the third value range is the value range of the target false recognition rate, and the fourth value range is the value range of the second feature; The first dimension reduction module is configured to reduce the dimension of the first original feature of the first sample to obtain the first feature according to the target equal error rate determined by the determination module, and recognize the target according to the target determination module determined by the determination module. The false rate reduces the dimension of the first original feature to obtain a second feature, and the first sample is a sample pre-stored or collected by the terminal; a storage module, configured to store the first feature and the second feature obtained by the dimensionality reduction module; an acquisition module, configured to acquire a biological image of the object to be identified, generate a second sample, and extract a second original feature of the second sample; The second dimension reduction module is configured to reduce the dimension of the second original feature obtained by the obtaining module to obtain a third feature according to the target equal error rate determined by the determining module, and obtain a third feature according to the target equal error rate determined by the determining module. The target false recognition rate reduces the dimension of the second original feature obtained by the obtaining module to obtain a fourth feature; an identification module, configured to compare and identify the third feature obtained by the second dimension reduction module and the first feature obtained by the first dimension reduction module The fourth feature obtained by the dimensionality reduction module and the second feature obtained by the dimensionality reduction of the first dimensionality reduction module are compared and identified to obtain an identification result. 8. The terminal according to claim 7, wherein the identification module comprises: a first judging unit, configured to judge whether the similarity between the third feature and the first feature is less than a first threshold; a second judging unit, configured to judge the fourth feature and the second feature if the first judging unit judges that the similarity between the third feature and the first feature is not less than the first threshold Whether it is less than the second threshold; A first identifying unit, configured to judge that the second sample is the same as the first sample if the second judgment judges that the similarity between the fourth feature and the second feature is not less than a second threshold. 9. The terminal according to claim 8, wherein the identification module further comprises: a second identifying unit, configured to judge the second sample and the first sample if the first judging unit judges that the similarity between the third feature and the first feature is less than the first threshold Are not the same. 10. The terminal according to claim 8, wherein the identification module further comprises: a third identifying unit, configured to judge, by the terminal, that the second sample is the same as the first if the second judging unit judges that the similarity between the fourth feature and the second feature is less than a second threshold This is not the same. 11. The terminal according to any one of claims 7 to 10, wherein the storage module comprises: a first quantization unit, configured to quantify the first feature according to the relational formula to obtain the first quantized feature, and quantify the second feature according to the relational formula to obtain the second quantized feature; a storage unit for storing the first quantization feature and the second quantization feature obtained by the quantization unit quantization; The identification module includes: a second quantization unit, configured to quantify the third feature according to the relational formula to obtain a third quantized feature, and quantify the fourth feature according to the relational formula to obtain a fourth quantized feature; The fourth identification unit is configured to compare and identify the third quantitative feature with the first quantitative feature, and compare and identify the fourth quantitative feature with the second quantitative feature to obtain an identification result. 12. The terminal according to claim 11, wherein the relational formula is: The V is the feature value of the sample after dimensionality reduction, the _Vmin is the minimum feature value after the sample dimensionality reduction, the _Vmax is the maximum feature value after the sample dimensionality reduction, and the value of N is 255 or 65535. 13. A terminal, characterized in that, comprising: transceivers, processors, memories and buses; the transceiver, the processor and the memory are connected through the bus; The processor has the following functions: determining a target equal error rate and a target false recognition rate, and the target equal error rate is a minimum equal error rate within the first value range so that the first feature does not exceed the second value range, so The first value range is the value range of the target equal error rate, the second value range is the value range of the first feature; the target false positive rate is within the third value range such that The second feature does not exceed the minimum false recognition rate of the fourth value range, the third value range is the value range of the target false recognition rate, and the fourth value range is the value range of the second feature value range; reduce the dimension of the first original feature of the first sample according to the target equal error rate to obtain the first feature, and reduce the dimension of the first original feature to obtain the second feature according to the target false recognition rate, so The first sample is a sample pre-stored or collected by the terminal; The memory has the following functions: storing the first feature and the second feature; The transceiver has the following functions: obtaining a biological image of the object to be identified to generate a second sample and extracting a second original feature of the second sample; The processor has the following functions: reducing the dimension of the second original feature according to the target equal error rate to obtain a third feature, and reducing the dimension of the second original feature according to the target false recognition rate to obtain a fourth feature ; Compare and identify the third feature with the first feature and compare and identify the fourth feature with the second feature to obtain an identification result.