CN112633250A - Face recognition detection experimental method and device - Google Patents
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Abstract
The invention provides a face recognition detection experimental method and a face recognition detection experimental device, and relates to the technical field of face recognition. The method comprises the following steps: acquiring a face image to be recognized, and performing Gaussian difference filtering processing on the face image to be recognized to obtain a filtered face image to be recognized; detecting at least one key point in the filtered face image to be recognized; extracting the characteristics of each key point in at least one key point by adopting a local binary pattern algorithm; firstly, Gaussian difference filtering processing is carried out on the obtained face image, and then feature extraction is carried out by adopting a local binary pattern algorithm, so that not only is the accuracy of the image ensured, but also the efficiency of image processing is improved. Then, the key points in the face image to be recognized are detected, and corresponding target key points are matched in each face template according to the characteristics of each key point, so that the face can be recognized accurately when the face templates are few through recognition.
Description
Technical Field
The invention relates to the technical field of face recognition, in particular to a face recognition detection experimental method and a face recognition detection experimental device.
Background
In recent years, a face recognition technology is rapidly developed and widely applied, and the face recognition technology is a technology for 'recognizing' identity by analyzing a face image by using a computer or an embedded device based on a known face library and further extracting effective recognition information by using a feature extraction technology. Currently, the commonly used face recognition is to compare a face to be recognized with a known face to obtain related information of a similarity degree. For example, the camera unit continuously collects face image information, and then determines whether face recognition is passed or not by positioning, extracting face features and comparing the extracted face features with all face templates in the face feature library.
With the continuous popularization in practical application, the problems of the face recognition system are gradually highlighted. For example, the face features have variability, such as various additives, changes of facial expressions, and the like, and the sample size of the face recognition system in a high-speed rail station is insufficient, and these problems may cause the face recognition system to generate recognition rejection and misrecognition phenomena in practical applications.
Disclosure of Invention
The invention aims to provide a face recognition detection experimental method and a face recognition detection experimental device, which are used for solving the problem of inaccurate face recognition in the prior art.
In a first aspect, an embodiment of the present application provides an experimental method for face recognition detection, where the method includes: acquiring a face image to be recognized, and performing Gaussian difference filtering processing on the face image to be recognized to obtain a filtered face image to be recognized; detecting at least one key point in the filtered face image to be recognized; extracting the characteristics of each key point in at least one key point by adopting a local binary pattern algorithm; matching target key points in each face template corresponding to each key point according to the characteristics of each key point; and identifying the face image to be identified according to the characteristics of the at least one key point and the characteristics of the at least one target key point matched from each face template.
In the implementation process, the Gaussian difference filtering processing is firstly carried out on the obtained face image, and then the local binary pattern algorithm is adopted for feature extraction, so that the accuracy of the image is ensured, and the efficiency of image processing is improved. Then, detecting key points in the face image to be recognized, and matching corresponding target key points in each face template according to the features of each key point, so as to recognize the face image to be recognized through the features of the key points in the face image to be recognized and the features of the target key points matched in each face template, so that under the condition of small registration samples, namely less face templates of each person, the corresponding target key points can be matched through the key points in the face image, and the face image to be recognized is accurately recognized.
In some embodiments of the present invention, before the step of detecting at least one keypoint in the filtered face image to be recognized, the method further comprises: comparing the face image to be recognized with a pre-stored face image sample set, comparing the filtered face image to be recognized with a pre-stored face image sample set subjected to Gaussian difference filtering, and finding out a recognition object corresponding to the face image to be recognized from the face image sample set; calculating the dispersion degree SCI of the total reconstruction coefficient of the face image to be recognized corresponding to the recognition object and the filtered face image to be recognized; and judging whether the face image to be recognized is a registered face image or not according to the total reconstruction coefficient dispersion degree SCI.
In some embodiments of the present invention, before the step of performing gaussian differential filtering processing on the face image to be recognized, the method includes: and carrying out normalization processing on the face image to be recognized.
In some embodiments of the present invention, before the step of obtaining the face image to be recognized, the method further includes: acquiring an image comprising a human face, and acquiring human face illumination data in the image; preprocessing an image comprising a human face according to human face illumination data, and extracting human face features; and carrying out global face detection according to the face features to detect whether the image of the face is a registered face image.
In some embodiments of the present invention, the step of performing global face detection according to the face features includes: identifying the face features by adopting a plurality of different detection schemes; judging whether the candidate face templates obtained by different detection schemes are the faces of the same person or not; if so, the human face features are detected through the global human face, and whether the image of the human face is the registered human face image is determined.
In a second aspect, an embodiment of the present application provides a face recognition detection experimental apparatus, and the apparatus includes: the image to be recognized acquisition module is used for acquiring a face image to be recognized and carrying out Gaussian difference filtering processing on the face image to be recognized to obtain a filtered face image to be recognized; the key point detection module is used for detecting at least one key point in the filtered face image to be recognized; the characteristic extraction module is used for extracting the characteristic of each key point in at least one key point by adopting a local binary pattern algorithm; the target key point matching module is used for matching target key points in each face template corresponding to each key point according to the characteristics of each key point; and the face recognition module is used for recognizing the face image to be recognized according to the characteristics of at least one key point and the characteristics of at least one target key point matched from each face template.
In some embodiments of the invention, the apparatus further comprises: the identification object determining module is used for comparing the face image to be identified with a pre-stored face image sample set, comparing the filtered face image to be identified with a pre-stored face image sample set which is subjected to Gaussian difference filtering, and finding out an identification object corresponding to the face image to be identified from the face image sample set; the computing module is used for computing the dispersion degree SCI of the total reconstruction coefficient of the face image to be recognized corresponding to the recognition object and the filtered face image to be recognized; and the registered face image judging module is used for judging whether the face image to be recognized is the registered face image or not according to the total reconstruction coefficient dispersion degree SCI.
In some embodiments of the invention, an apparatus comprises: and the normalization processing module is used for performing normalization processing on the face image to be recognized.
In some embodiments of the invention, the apparatus further comprises: the illumination data acquisition module is used for acquiring an image comprising a human face and acquiring human face illumination data in the image; the preprocessing module is used for preprocessing the image comprising the face according to the face illumination data and extracting the face features; and the global detection module is used for carrying out global face detection according to the face features so as to detect whether the image of the face is a registered face image.
In some embodiments of the invention, the global detection module comprises: the human face feature recognition unit is used for recognizing the human face features by adopting a plurality of different detection schemes; the face template judging unit is used for judging whether the candidate face templates obtained by different detection schemes are the faces of the same person or not; and the global detection unit is used for detecting the face features through the global face if the face features are the registered face images, and determining whether the face images are the registered face images.
In a third aspect, an embodiment of the present application provides an electronic device, which includes a memory for storing one or more programs; a processor. The program or programs, when executed by a processor, implement the method of any of the first aspects as described above.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the method according to any one of the first aspect described above.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of an experimental method for face recognition detection according to an embodiment of the present invention;
fig. 2 is a block diagram of a structure of an experimental apparatus for face recognition detection according to an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.
Icon: 100-a face recognition detection experimental device; 110-an image acquisition module to be identified; 120-key point detection module; 130-a feature extraction module; 140-target keypoint matching module; 150-a face recognition module; 101-a memory; 102-a processor; 103-communication interface.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the individual features of the embodiments can be combined with one another without conflict.
Referring to fig. 1, fig. 1 is a flowchart of an experimental method for face recognition detection according to an embodiment of the present invention. The embodiment of the application provides a face recognition detection experimental method, which comprises the following steps:
step S110: and acquiring a face image to be recognized, and performing Gaussian difference filtering processing on the face image to be recognized to obtain a filtered face image to be recognized.
Since most of the noise in an image belongs to gaussian noise, gaussian differential filtering is often applied to reduce the noise of the image. The gaussian difference filtering is a linear smooth filtering, is suitable for eliminating gaussian noise, and is widely applied to the noise reduction process of image processing. The gaussian difference filtering is a process of weighted average of the whole image, and the value of each pixel point is obtained by weighted average of the pixel point and other pixel values in the neighborhood. The specific operation of the gaussian difference filtering is: each pixel in the image is scanned using a template (or convolution, mask), and the weighted average gray value of the pixels in the neighborhood determined by the template is used to replace the value of the pixel in the center of the template. The noise reduction processing of the image is realized through the filtered face image to be recognized obtained through Gaussian difference filtering, and then the accuracy of recognition can be ensured by recognizing the filtered face image to be recognized.
Step S120: and detecting at least one key point in the filtered face image to be recognized.
Step S130: and extracting the characteristics of each key point in at least one key point by adopting a local binary pattern algorithm.
The Local Binary Pattern (LBP) algorithm is an operator used to describe the Local texture features of an image; it has the obvious advantages of rotation invariance, gray scale invariance and the like. It is prepared with T.Ojala,
Harwood was proposed in 1994 for texture feature extraction, and the extracted features are local texture features of the image. The algorithm is simple, but can be very efficient to perform the featuresTherefore, the efficiency of face recognition can be ensured by adopting a local binary pattern algorithm to extract the characteristics of each key point in at least one key point.
Step S140: and matching the target key points in each face template corresponding to each key point according to the characteristics of each key point.
Step S150: and identifying the face image to be identified according to the characteristics of the at least one key point and the characteristics of the at least one target key point matched from each face template.
Since the positions of the key points in the face image are known, for example, the key points around the eyes of the face, the face image to be recognized is recognized by detecting the key points in the face image to be recognized and matching the corresponding target key points in each face template according to the features of each key point so as to match the features of the key points in the face image to be recognized and the features of the target key points matched in each face template, so that the face image to be recognized can be accurately recognized by matching the key points in the face image to the corresponding target key points in the small registration sample, that is, under the condition that the face templates of each person are few, and the problems of high recognition error rate and even no recognition caused by the fact that the face templates of each person are few are avoided. Meanwhile, when the resolution of the face image to be recognized is small, the face key points can be accurately detected, and the recognition rate of the small-resolution face image is improved.
The step of recognizing the face image to be recognized according to the feature of the at least one key point and the feature of the at least one target key point matched from each face template may specifically be: firstly, generating the characteristics of a face image to be recognized according to the characteristics of at least one key point, and generating the characteristics of each face template according to the characteristics of at least one target key point; calculating the Euclidean distance between the features of the face image to be recognized and the features of each face template; and selecting the face template with the shortest Euclidean distance between the features and the features of the face image to be recognized as the recognized face image.
The feature of each key point and the feature of each target key point can be represented by a vector, when a plurality of key points are detected in the face image to be recognized, the features of the plurality of key points can be connected in series, that is, the plurality of vectors are connected in series to obtain the feature of the face image to be recognized, wherein the feature is a vector. In addition, the features of the target key points matched from each face template can be connected in series to obtain the feature of each face template, and the feature is also a vector. And finally, solving the Euclidean distance by the characteristics of the face image to be recognized and the characteristics of each face template so as to search the face template matched with the face image to be recognized from the face template.
In the above technical solution, it may be determined whether an euclidean distance between the features of the recognized face image and the features of the face image to be recognized is greater than or equal to a predetermined value, if so, it is determined that the recognition is failed, otherwise, it is determined that the recognition is successful.
The method and the device can reduce the false recognition rate of the face image and improve the accuracy of the face image recognition by judging whether the Euclidean distance between the features of the recognized face image and the features of the face image to be recognized is larger than or equal to a preset numerical value or not. Specifically, when a face template matching the face image to be recognized does not exist in the face template, that is, when a registered sample of the face image to be recognized is not stored, a recognition result is obtained by calculating and comparing the euclidean distance, which causes a recognition error, so that the euclidean distance can be compared with a predetermined value to improve the accuracy of face image recognition.
In some embodiments of the present invention, the face image to be recognized may be pre-recognized first, and two methods provided in the present application are described below.
The first pre-recognition method comprises the steps of comparing a face image to be recognized with a pre-stored face image sample set, comparing a filtered face image to be recognized with a pre-stored face image sample set which is subjected to Gaussian difference filtering, finding out a recognition object corresponding to the face image to be recognized from the face image sample set, and then calculating the dispersion degree SCI of the total reconstruction coefficient of the face image to be recognized corresponding to the recognition object and the filtered face image to be recognized. And finally, judging whether the face image to be recognized is a registered face image according to the dispersion degree SCI of the overall reconstruction coefficient.
And when the SCI value is larger than a preset value, determining the face image to be recognized as a registered face image, otherwise, determining the face image to be recognized as a non-registered face image. And the aim of adjusting the identification precision according to different environments is fulfilled by setting different preset values.
As an implementation manner in this embodiment, before the step of performing the gaussian difference filtering processing on the face image to be recognized, normalization processing may be performed on the face image to be recognized.
The second pre-recognition method comprises the steps of obtaining an image including a human face and human face illumination data before the step of obtaining a human face image to be recognized, then preprocessing the image including the human face according to the human face illumination data, and extracting human face features. And then carrying out global face detection according to the face features to detect whether the image of the face is a registered face image.
The illumination data may include light information of the face image. For example, a plurality of face images can be obtained, an effective face image can be obtained from the face images, and light information can be collected to obtain the illumination intensity at the moment. Then, the human image is preprocessed according to the illumination data, and the human face features are extracted. Specifically, histogram equalization processing can be performed according to the acquired face image and the illumination data, then the face is normalized to a uniform size, and then the face features are extracted according to a local binary pattern algorithm. Then carrying out global face detection according to the face features, and if the face features pass the global face detection, indicating that the image of the face is a registered face image; if not, the face image is a non-registered face image.
When global face detection is performed according to the face features, a plurality of different detection schemes can be adopted to identify the face features, then whether the candidate face templates obtained by the different detection schemes are the faces of the same person or not is judged, if yes, the face features are detected through the global face, and whether the face images are the registered face images or not is determined.
Based on the same inventive concept, the present invention further provides a face recognition detection experimental apparatus 100, please refer to fig. 2, fig. 2 is a structural block diagram of the face recognition detection experimental apparatus provided by the embodiment of the present invention, and the face recognition detection experimental apparatus 100 includes:
the image to be recognized acquiring module 110 is configured to acquire a face image to be recognized, and perform gaussian difference filtering on the face image to be recognized to obtain a filtered face image to be recognized;
a key point detection module 120, configured to detect at least one key point in the filtered face image to be recognized;
a feature extraction module 130, configured to extract a feature of each key point in at least one key point by using a local binary pattern algorithm;
a target key point matching module 140, configured to match a target key point in each face template corresponding to each key point according to the feature of each key point;
and the face recognition module 150 is configured to recognize a face image to be recognized according to the feature of the at least one key point and the feature of the at least one target key point matched from each face template.
In some embodiments of the invention, the apparatus further comprises:
the identification object determining module is used for comparing the face image to be identified with a pre-stored face image sample set, comparing the filtered face image to be identified with a pre-stored face image sample set which is subjected to Gaussian difference filtering, and finding out an identification object corresponding to the face image to be identified from the face image sample set;
the computing module is used for computing the dispersion degree SCI of the total reconstruction coefficient of the face image to be recognized corresponding to the recognition object and the filtered face image to be recognized;
and the registered face image judging module is used for judging whether the face image to be recognized is the registered face image or not according to the total reconstruction coefficient dispersion degree SCI.
In some embodiments of the invention, an apparatus comprises:
and the normalization processing module is used for performing normalization processing on the face image to be recognized.
In some embodiments of the invention, the apparatus further comprises:
the illumination data acquisition module is used for acquiring an image comprising a human face and acquiring human face illumination data in the image;
the preprocessing module is used for preprocessing the image comprising the face according to the face illumination data and extracting the face features;
and the global detection module is used for carrying out global face detection according to the face features so as to detect whether the image of the face is a registered face image.
In some embodiments of the invention, the global detection module comprises:
the human face feature recognition unit is used for recognizing the human face features by adopting a plurality of different detection schemes;
the face template judging unit is used for judging whether the candidate face templates obtained by different detection schemes are the faces of the same person or not;
and the global detection unit is used for detecting the face features through the global face if the face features are the registered face images, and determining whether the face images are the registered face images.
Referring to fig. 3, fig. 3 is a schematic structural block diagram of an electronic device according to an embodiment of the present disclosure. The electronic device comprises a memory 101, a processor 102 and a communication interface 103, wherein the memory 101, the processor 102 and the communication interface 103 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules, such as program instructions/modules corresponding to the face recognition testing device 100 provided in the embodiments of the present application, and the processor 102 executes the software programs and modules stored in the memory 101, so as to execute various functional applications and data processing. The communication interface 103 may be used for communicating signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 102 may be an integrated circuit chip having signal processing capabilities. The Processor 102 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
It will be appreciated that the configuration shown in fig. 3 is merely illustrative and that the electronic device may include more or fewer components than shown in fig. 3 or have a different configuration than shown in fig. 3. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted 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-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In summary, the present application provides a face recognition detection experimental method and apparatus, and the method includes: acquiring a face image to be recognized, and performing Gaussian difference filtering processing on the face image to be recognized to obtain a filtered face image to be recognized; detecting at least one key point in the filtered face image to be recognized; extracting the characteristics of each key point in at least one key point by adopting a local binary pattern algorithm; matching target key points in each face template corresponding to each key point according to the characteristics of each key point; and identifying the face image to be identified according to the characteristics of the at least one key point and the characteristics of the at least one target key point matched from each face template. Firstly, Gaussian difference filtering processing is carried out on the obtained face image, and then feature extraction is carried out by adopting a local binary pattern algorithm, so that not only is the accuracy of the image ensured, but also the efficiency of image processing is improved. Then, detecting key points in the face image to be recognized, and matching corresponding target key points in each face template according to the features of each key point, so as to recognize the face image to be recognized through the features of the key points in the face image to be recognized and the features of the target key points matched in each face template, so that under the condition of small registration samples, namely less face templates of each person, the corresponding target key points can be matched through the key points in the face image, and the face image to be recognized is accurately recognized.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. An experimental method for face recognition detection, the method comprising:
acquiring a face image to be recognized, and performing Gaussian difference filtering processing on the face image to be recognized to obtain a filtered face image to be recognized;
detecting at least one key point in the filtered face image to be recognized;
extracting the characteristics of each key point in the at least one key point by adopting a local binary pattern algorithm;
matching target key points in each face template corresponding to each key point according to the characteristics of each key point;
and identifying the face image to be identified according to the characteristics of the at least one key point and the characteristics of the at least one target key point matched from each face template.
2. The method according to claim 1, wherein the step of detecting at least one keypoint in the filtered face image to be recognized is preceded by the method further comprising:
comparing the facial image to be recognized with a pre-stored facial image sample set, comparing the filtered facial image to be recognized with a pre-stored facial image sample set subjected to Gaussian difference filtering, and finding out a recognition object corresponding to the facial image to be recognized from the facial image sample set;
calculating the dispersion degree SCI of the overall reconstruction coefficient of the to-be-recognized face image corresponding to the recognition object and the filtered to-be-recognized face image;
and judging whether the face image to be recognized is a registered face image or not according to the total reconstruction coefficient dispersion degree SCI.
3. The method according to claim 1, wherein before the step of performing gaussian differential filtering processing on the face image to be recognized, the method comprises:
and carrying out normalization processing on the face image to be recognized.
4. The method of claim 1, wherein the step of obtaining the image of the face to be recognized is preceded by the method further comprising:
acquiring an image comprising a human face, and acquiring human face illumination data in the image;
preprocessing the image comprising the face according to the face illumination data, and extracting face features;
and carrying out global face detection according to the face features to detect whether the image of the face is a registered face image.
5. The method of claim 4, wherein the step of performing global face detection based on the face features comprises:
adopting a plurality of different detection schemes to identify the human face features;
judging whether the candidate face templates obtained by different detection schemes are the faces of the same person or not;
if so, the human face features are detected through the global human face, and whether the image of the human face is a registered human face image is determined.
6. An experimental apparatus for face recognition and detection, the apparatus comprising:
the system comprises a to-be-recognized image acquisition module, a face recognition module and a face recognition module, wherein the to-be-recognized image acquisition module is used for acquiring a face image to be recognized and carrying out Gaussian difference filtering processing on the face image to be recognized to obtain a filtered face image to be recognized;
the key point detection module is used for detecting at least one key point in the filtered face image to be recognized;
the characteristic extraction module is used for extracting the characteristic of each key point in the at least one key point by adopting a local binary pattern algorithm;
the target key point matching module is used for matching target key points in each face template corresponding to each key point according to the characteristics of each key point;
and the face recognition module is used for recognizing the face image to be recognized according to the characteristics of the at least one key point and the characteristics of the at least one target key point matched from each face template.
7. The apparatus of claim 6, further comprising:
the identification object determining module is used for comparing the facial image to be identified with a pre-stored facial image sample set, comparing the filtered facial image to be identified with a pre-stored facial image sample set which is subjected to Gaussian difference filtering, and finding out an identification object corresponding to the facial image to be identified from the facial image sample set;
the calculation module is used for calculating the dispersion degree SCI of the overall reconstruction coefficient of the face image to be recognized corresponding to the recognition object and the filtered face image to be recognized;
and the registered face image judging module is used for judging whether the face image to be recognized is a registered face image or not according to the total reconstruction coefficient dispersion degree SCI.
8. The apparatus of claim 6, wherein the apparatus comprises:
and the normalization processing module is used for performing normalization processing on the face image to be recognized.
9. An electronic device, comprising:
a memory for storing one or more programs;
a processor;
the one or more programs, when executed by the processor, implement the method of any of claims 1-5.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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