CN114120382B - Face recognition system testing method and device, electronic equipment and medium - Google Patents
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Abstract
One or more embodiments of the present disclosure provide a method, an apparatus, an electronic device, and a medium for testing a face recognition system, where a face sample library integrating a public face sample library, a real-time face sample library, and a simulated face sample library is constructed, so as to obtain abundant and multi-feature face samples, simulate different light and angle environments by using a display device, register and identify the face samples through the face recognition system to be tested, and calculate a registration success rate and an identification accuracy rate, so as to accurately and efficiently evaluate the face recognition system to be tested.
Description
Technical Field
One or more embodiments of the present disclosure relate to the field of computer face recognition technology, and in particular, to a method, an apparatus, an electronic device, and a medium for testing a face recognition system.
Background
Face recognition is a widely applied biological feature recognition technology, and can be used for carrying out face detection and tracking on a static image or a video image, further carrying out feature extraction by methods of geometric features, algebraic features, fixed feature templates, feature faces and the like of the face image, mapping the face image into a low-dimensional space, and adopting a certain matching strategy to compare the face image with the known face in the existing database so as to recognize the identity of the face. In the practical application of face recognition, the accuracy of face recognition is a key index for evaluating a face recognition system, and the accuracy of face recognition is often closely related to factors such as face image acquisition equipment, face size, attitude angle, age change, image noise, application environment light and the like.
The existing face recognition accuracy testing means mainly comprise two kinds, one is to manually play a still picture or a video image to a camera in a face recognition system through a PC (personal computer) display, and to conduct face recognition comparison testing by adjusting the angle of the camera; the other is a camera of the face recognition system, which is faced by a true person of a tester, and the tester carries out face recognition living body detection by adjusting different postures. The testing method has the defects of low testing efficiency, the need of the test personnel to watch at any time and the need of manually analyzing the testing result, and a great deal of labor cost is required to be consumed.
Disclosure of Invention
In view of this, an object of one or more embodiments of the present disclosure is to provide a method, an apparatus, an electronic device and a medium for testing a face recognition system, so as to solve the problems of low testing efficiency and large manpower cost consumption of the existing testing means for accuracy of face recognition.
In view of the above objects, one or more embodiments of the present disclosure provide a method for testing a face recognition system, including:
acquiring a face sample library; the human face sample library comprises a public human face sample library, a real-time human face sample library and a simulation human face sample library;
playing the face samples in the face sample library by using a display device;
identifying the face sample by using a detected face recognition system to obtain a test result;
and evaluating the face recognition system to be tested according to the test result.
Optionally, the obtaining a face sample library includes:
constructing the common face sample library according to at least one face database of FERET face databases, YALE face databases, CMU-PIE face databases, ORL face databases and CAS-PEAL large-scale face databases;
Taking photos and videos of true persons of different sexes and ages under different light rays and different angles, wherein the photos comprise single-person photos, multi-person photos, head portrait photos and whole-body photos, and constructing the real shooting face sample library according to the photos and videos;
and manufacturing a 3D simulated face animation by using a 3D modeling tool, and constructing the simulated face sample library according to the 3D simulated face animation.
Optionally, the playing the face sample in the face sample library by using a display device specifically includes:
and in the playing process, controlling the display equipment to display different brightness so as to simulate scenes with different light rays.
Optionally, the playing the face sample in the face sample library by using a display device specifically includes:
And in the playing process, adjusting different angles of the 3D simulation face animation to simulate different recognition angles.
Optionally, the identifying the face sample by using the detected face recognition system to obtain a test result specifically includes:
Registering and registering the face sample by using the detected face recognition system, and feeding back a registration and registration result;
Identifying the face sample by using the detected face recognition system with the registration, and feeding back an identification result;
And taking the registration result and the identification result as test results.
Optionally, the identification result includes:
face recognition success, face recognition failure, face unregistered or face information undetected.
Optionally, the evaluating the face recognition system to be tested according to the test result includes:
Calculating the registration success rate and the identification accuracy rate;
And evaluating the detected face recognition system according to a preset registration success rate threshold and a preset recognition accuracy rate threshold.
Based on the same inventive concept, one or more embodiments of the present disclosure provide a testing device of a face recognition system, including:
The sample acquisition module is used for acquiring a face sample library; the human face sample library comprises a public human face sample library, a real-time human face sample library and a simulation human face sample library;
the sample output module is used for playing the face samples in the face sample library by using display equipment;
the test result acquisition module is used for identifying the face sample by using a face recognition system to be tested to obtain a test result;
And the system evaluation module is used for evaluating the face recognition system to be tested according to the test result.
Based on the same inventive concept, one or more embodiments of the present specification provide an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the above-described method when executing the program.
Based on the same inventive concept, one or more embodiments of the present specification provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the above-described method.
From the foregoing, it can be seen that, according to one or more embodiments of the present disclosure, a method, an apparatus, an electronic device, and a medium for testing a face recognition system are provided, by constructing a face sample library that integrates a public face sample library, a real-time face sample library, and a simulated face sample library, obtaining abundant and multi-feature face samples, simulating different light and angle environments by using a display device, registering and recognizing the face samples through a detected face recognition system, and calculating a registration success rate and a recognition accuracy rate, so as to accurately and efficiently evaluate the detected face recognition system.
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For a clearer description of one or more embodiments of the present description or of the solutions of the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are only one or more embodiments of the present description, from which other drawings can be obtained, without inventive effort, for a person skilled in the art.
Fig. 1 is a schematic flow chart of a testing method of a face recognition system according to one or more embodiments of the present disclosure;
fig. 2 is a schematic structural diagram of a testing device of a face recognition system according to one or more embodiments of the present disclosure;
fig. 3 is a schematic diagram of a hardware structure of an electronic device according to one or more embodiments of the present disclosure.
Detailed Description
For the purposes of promoting an understanding of the principles and advantages of the disclosure, reference will now be made to the embodiments illustrated in the drawings and specific language will be used to describe the same.
It is noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present disclosure should be taken in a general sense as understood by one of ordinary skill in the art to which the present disclosure pertains. The use of the terms "first," "second," and the like in one or more embodiments of the present description does not denote any order, quantity, or importance, but rather the terms "first," "second," and the like are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
Fig. 1 is a schematic flow chart of a testing method of a face recognition system according to one or more embodiments of the present disclosure, where the testing method of the face recognition system includes:
S101, acquiring a face sample library.
The face sample library comprises: a public face sample library, a real-time face sample library and a simulation face sample library.
In some embodiments, obtaining a common face sample library includes:
And constructing the common face sample library according to at least one face database of FERET face databases, YALE face databases, CMU-PIE face databases, ORL face databases and CAS-PEAL large-scale face databases.
Wherein FERET faces database originated from the united states and was created by FERET item, the image set contains a large number of face images, and there is only one face in each figure. The concentration, the photo of the same person has different expression, illumination, posture and age changes. The face image containing more than 1 ten thousand face images with multiple postures and illumination is one of face databases widely applied in the face recognition field, and most of the face images are western people.
YALE face library contains 5850 multi-pose and multi-illumination images of 10 people. The images of the gesture and the illumination change are collected under the strictly controlled condition, and are mainly used for modeling and analyzing the illumination and the gesture problems.
CMU-PIE face libraries, so-called PIE, are abbreviations for pose (pose), illumination (Illumination), and Expression (Expression). Facial images containing 41368 multi-poses, illumination, and expression of 68 volunteers. The posture and illumination change images are collected under the condition of strict control, and the method is an important test set in the field of face recognition. The method comprises 13 posture conditions, 43 illumination conditions and 4 photo under expression of each person, and the existing multi-posture face recognition documents are basically tested on a CMU-PIEPIE face library.
The ORL face library was created by AT & T laboratories, university of cambridge, england, and contained 400 facial images of 40 total, with images of part of the volunteers including changes in pose, expression, and face ornaments. Each acquisition object in all sample libraries of one acquisition object in the ORL face database comprises 10 gray-scale images subjected to normalization processing, the image size is 92 multiplied by 112, and the image background is black. The facial expression and details of the collected subject are changed, such as laughing and laughing, eyes are open or closed, and eyes are worn or not worn, and the gestures of different face samples are also changed, and the depth rotation and plane rotation of the face samples can reach 20 degrees.
CAS-PEAL is a database of 99450 face pictures of workers who contained 1040 volunteers, completed in 2003 by the institute of computing technology at the department of Chinese sciences. The database covers changes in features such as pose, expression, decoration, illumination, background, distance, and time.
The public face database is downloaded through the internet technology and comprises a plurality of face sample data with multiple types, multiple characteristics and huge quantity, so that the public face sample database is constructed.
In some embodiments, obtaining a sample library of real-time face samples includes:
And shooting photos and videos of true persons of different sexes and ages under different light rays and different angles, wherein the photos comprise single-person photos, multi-person photos, head portrait photos and whole-body photos, and constructing the real shooting face sample library according to the photos and videos.
Different face recognition systems have different pertinence, such as a vehicle-mounted face recognition system and the like, the faced identified groups are different, in this case, the face recognition system obtained by training or testing only based on a public face database may not have good effect in the actual use environment, so that the face recognition system is suitable for the applicable groups of the face recognition system, the volunteers of the groups are summoned, photos and videos of true persons with different sexes and different ages under different light rays and different angles are taken, and the photos comprise single photo, multiple photo, head photo and whole body photo, so that a face sample library close to the use environment is constructed.
In some embodiments, obtaining a library of simulated face samples includes:
and manufacturing a 3D simulated face animation by using a 3D modeling tool, and constructing the simulated face sample library according to the 3D simulated face animation.
In order to achieve a better simulation effect, namely to be closer to a scene of real face recognition, the real face is likely to have multiple angles when recognizing the face, so that a certain simulation effect can be achieved only by shooting pictures of multiple rays and multiple postures for one face, but a large number of pictures with different angles are needed, the workload is huge, the existing methods are used for avoiding shooting a large number of pictures, a face model is made into a real object, and the different angles of the face model are rotated through some mechanical equipment, so that a certain simulation effect is achieved, but the method consumes manpower and material resources, and the number of samples is extremely limited due to the fact that the real object model is used.
The method adopts a 3D modeling mode, so that a large number of photos at different angles are prevented from being shot, loss of manpower and material resources caused by objects is avoided, the situation that the face recognition scene is close to an actual face recognition scene is realized, and a good recognition effect is achieved.
S102, playing the face sample in the face sample library by using a display device.
In some embodiments, during the playing, the display device is controlled to display different brightness so as to simulate different light effects.
The brightness of the PC monitor is used to simulate various application scenes of the detected face recognition system under different light conditions, including but not limited to strong illumination, dark days, overcast and rainy days and the like.
In some embodiments, during the playing, different angles of the 3D simulated face animation are adjusted.
3D simulated face animation manufactured by 3D modeling software is utilized to simulate faces with different angles and partial living body detection characteristics, and various face recognition application scenes such as low head, blink, head-up, eye-closing, mouth opening, side face and the like are established.
S103, identifying the face sample by using a detected face recognition system to obtain a test result.
In some embodiments, the face sample is registered by using the detected face recognition system, and a registration result is fed back;
and identifying the face sample by using the detected face identification system with the registration, and feeding back an identification result.
And taking the registration result and the identification result as test results.
The identification result comprises:
face recognition success, face recognition failure, face unregistered, no face information detected.
The method and the device automatically design and generate test cases according to the arrangement combination and execute the test cases sequentially.
And the serial port and the Ethernet are utilized to carry out data communication with the face recognition system to be tested, the test system can send an instruction to the system to be tested through the serial port to directly remotely control the system to be tested, and the instruction execution result is recovered through the Ethernet.
One face image is registered once, whether the registration is successful or not, the next face image is registered until all faces to be registered are registered. The face registration times and the registration success times are received and recorded.
S104, evaluating the face recognition system to be tested according to the test result.
In some embodiments, a registration success rate and an identification accuracy rate are calculated;
And evaluating the detected face recognition system according to a preset registration success rate threshold and a preset recognition accuracy rate threshold.
From the foregoing, it can be seen that, according to one or more embodiments of the present disclosure, a method, an apparatus, an electronic device, and a medium for testing a face recognition system are provided, by constructing a face sample library that integrates a public face sample library, a real-time face sample library, and a simulated face sample library, obtaining abundant and multi-feature face samples, simulating different light and angle environments by using a display device, registering and recognizing the face samples through a detected face recognition system, and calculating a registration success rate and a recognition accuracy rate, so as to accurately and efficiently evaluate the detected face recognition system.
It is understood that the method may be performed by any apparatus, device, platform, cluster of devices having computing, processing capabilities.
It should be noted that the methods of one or more embodiments of the present description may be performed by a single device, such as a computer or server. The method of the embodiment can also be applied to a distributed scene, and is completed by mutually matching a plurality of devices. In the case of such a distributed scenario, one of the devices may perform only one or more steps of the methods of one or more embodiments of the present description, the devices interacting with each other to accomplish the methods.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Fig. 2 is a schematic structural diagram of a testing device of a face recognition system according to one or more embodiments of the present disclosure, where the testing device of the face recognition system includes:
A sample acquiring module 201, configured to acquire a face sample library; the face sample library comprises a public face sample library, a real-time face sample library and a simulation face sample library.
In some embodiments, it is specifically used to:
constructing the common face sample library according to at least one face database of FERET face databases, YALE face databases, CMU-PIE face databases, ORL face databases and CAS-PEAL large-scale face databases;
Taking photos and videos of true persons of different sexes and ages under different light rays and different angles, wherein the photos comprise single-person photos, multi-person photos, head portrait photos and whole-body photos, and constructing the real shooting face sample library according to the photos and videos;
and manufacturing a 3D simulated face animation by using a 3D modeling tool, and constructing the simulated face sample library according to the 3D simulated face animation.
And the sample output module 202 is used for playing the face samples in the face sample library by using a display device.
In some embodiments, it is specifically used to:
and in the playing process, controlling the display equipment to display different brightness so as to simulate scenes with different light rays.
And in the playing process, adjusting different angles of the 3D simulation face animation to simulate different recognition angles.
And the test result obtaining module 203 is configured to identify the face sample by using a face recognition system to be tested, so as to obtain a test result.
In some embodiments, it is specifically used to:
Registering and registering the face sample by using the detected face recognition system, and feeding back a registration and registration result;
Identifying the face sample by using the detected face recognition system with the registration, and feeding back an identification result;
And taking the registration result and the identification result as test results.
Wherein, the recognition result includes:
face recognition success, face recognition failure, face unregistered or face information undetected.
And the system evaluation module 204 is configured to evaluate the face recognition system to be tested according to the test result.
In some embodiments, it is specifically used to:
Calculating the registration success rate and the identification accuracy rate;
And evaluating the detected face recognition system according to a preset registration success rate threshold and a preset recognition accuracy rate threshold.
For convenience of description, the above devices are described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in one or more pieces of software and/or hardware when implementing one or more embodiments of the present description.
The device of the foregoing embodiment is configured to implement the corresponding method in the foregoing embodiment, and has the beneficial effects of the corresponding method embodiment, which is not described herein.
Fig. 3 shows a more specific hardware architecture of an electronic device according to this embodiment, where the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. Wherein processor 1010, memory 1020, input/output interface 1030, and communication interface 1040 implement communication connections therebetween within the device via a bus 1050.
The processor 1010 may be implemented by a general-purpose CPU (Central Processing Unit ), a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solutions provided in the embodiments of the present disclosure.
The Memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory ), static storage, dynamic storage, etc. Memory 1020 may store an operating system and other application programs, and when the embodiments of the present specification are implemented in software or firmware, the associated program code is stored in memory 1020 and executed by processor 1010.
The input/output interface 1030 is used to connect with an input/output module for inputting and outputting information. The input/output module may be configured as a component in a device (not shown) or may be external to the device to provide corresponding functionality. Wherein the input devices may include a keyboard, mouse, touch screen, microphone, various types of sensors, etc., and the output devices may include a display, speaker, vibrator, indicator lights, etc.
Communication interface 1040 is used to connect communication modules (not shown) to enable communication interactions of the present device with other devices. The communication module may implement communication through a wired manner (such as USB, network cable, etc.), or may implement communication through a wireless manner (such as mobile network, WIFI, bluetooth, etc.).
Bus 1050 includes a path for transferring information between components of the device (e.g., processor 1010, memory 1020, input/output interface 1030, and communication interface 1040).
It should be noted that although the above-described device only shows processor 1010, memory 1020, input/output interface 1030, communication interface 1040, and bus 1050, in an implementation, the device may include other components necessary to achieve proper operation. Furthermore, it will be understood by those skilled in the art that the above-described apparatus may include only the components necessary to implement the embodiments of the present description, and not all the components shown in the drawings.
The computer readable media of the present embodiments, including both permanent and non-permanent, removable and non-removable media, may be used to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the disclosure, including the claims, is limited to these examples; combinations of features of the above embodiments or in different embodiments are also possible within the spirit of the present disclosure, steps may be implemented in any order, and there are many other variations of the different aspects of one or more embodiments described above which are not provided in detail for the sake of brevity.
Additionally, well-known power/ground connections to Integrated Circuit (IC) chips and other components may or may not be shown within the provided figures, in order to simplify the illustration and discussion, and so as not to obscure one or more embodiments of the present description. Furthermore, the apparatus may be shown in block diagram form in order to avoid obscuring the one or more embodiments of the present description, and also in view of the fact that specifics with respect to implementation of such block diagram apparatus are highly dependent upon the platform within which the one or more embodiments of the present description are to be implemented (i.e., such specifics should be well within purview of one skilled in the art). Where specific details (e.g., circuits) are set forth in order to describe example embodiments of the disclosure, it should be apparent to one skilled in the art that one or more embodiments of the disclosure can be practiced without, or with variation of, these specific details. Accordingly, the description is to be regarded as illustrative in nature and not as restrictive.
While the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of those embodiments will be apparent to those skilled in the art in light of the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.
The present disclosure is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Any omissions, modifications, equivalents, improvements, and the like, which are within the spirit and principles of the one or more embodiments of the disclosure, are therefore intended to be included within the scope of the disclosure.
Claims (6)
1. A method for testing a face recognition system, comprising:
acquiring a face sample library; the face sample library comprises a public face sample library, a real-time face sample library and a 3D simulation face sample library;
playing the face sample in the face sample library by using a display device, which specifically comprises the following steps: in the playing process, controlling the display equipment to display different brightness so as to simulate scenes with different light rays; in the playing process, adjusting different angles of the 3D simulation face animation to simulate different recognition angles;
The face sample is identified by the face recognition system to be tested, and a test result is obtained, which comprises the following steps: registering and registering the face sample by using the detected face recognition system, and feeding back a registration and registration result; identifying the face sample by using the detected face recognition system with the registration, and feeding back an identification result; taking the registration result and the identification result as test results;
And evaluating the face recognition system to be tested according to the test result, wherein the method specifically comprises the following steps: calculating the registration success rate and the identification accuracy rate; and evaluating the detected face recognition system according to a preset registration success rate threshold and a preset recognition accuracy rate threshold.
2. The method according to claim 1, wherein the obtaining a face sample library comprises:
constructing the common face sample library according to at least one face database of FERET face databases, YALE face databases, CMU-PIE face databases, ORL face databases and CAS-PEAL large-scale face databases;
Taking photos and videos of true persons of different sexes and ages under different light rays and different angles, wherein the photos comprise single-person photos, multi-person photos, head portrait photos and whole-body photos, and constructing the real shooting face sample library according to the photos and videos;
And manufacturing a 3D simulated face animation by using a 3D modeling tool, and constructing the 3D simulated face sample library according to the 3D simulated face animation.
3. The test method of claim 1, wherein the recognition result comprises:
face recognition success, face recognition failure, face unregistered or face information undetected.
4. A test device for a face recognition system, comprising:
the sample acquisition module is used for acquiring a face sample library; the face sample library comprises a public face sample library, a real-time face sample library and a 3D simulation face sample library;
the sample output module is used for playing the face samples in the face sample library by using the display equipment, and is specifically used for: in the playing process, controlling the display equipment to display different brightness so as to simulate scenes with different light rays; in the playing process, adjusting different angles of the 3D simulation face animation to simulate different recognition angles;
The test result acquisition module is used for identifying the face sample by using the face recognition system to be tested to obtain a test result, and is specifically used for: registering and registering the face sample by using the detected face recognition system, and feeding back a registration and registration result; identifying the face sample by using the detected face recognition system with the registration, and feeding back an identification result; taking the registration result and the identification result as test results;
The system evaluation module is used for evaluating the face recognition system to be tested according to the test result, and is specifically used for: calculating the registration success rate and the identification accuracy rate; and evaluating the detected face recognition system according to a preset registration success rate threshold and a preset recognition accuracy rate threshold.
5. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 3 when the program is executed by the processor.
6. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1 to 3.
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| CN202010863669.9A CN114120382B (en) | 2020-08-25 | 2020-08-25 | Face recognition system testing method and device, electronic equipment and medium |
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| CN202010863669.9A CN114120382B (en) | 2020-08-25 | 2020-08-25 | Face recognition system testing method and device, electronic equipment and medium |
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