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GB/T 41772-2022 English PDF

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GB/T 41772-2022: Information technology - Biometrics - Technical requirements for face recognition system
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Basic data

Standard ID: GB/T 41772-2022 (GB/T41772-2022)
Description (Translated English): Information technology - Biometrics - Technical requirements for face recognition system
Sector / Industry: National Standard (Recommended)
Classification of Chinese Standard: L67
Classification of International Standard: 35.240.15
Word Count Estimation: 18,120
Date of Issue: 2022-10-14
Date of Implementation: 2023-05-01
Issuing agency(ies): State Administration for Market Regulation, China National Standardization Administration

GB/T 41772-2022: Information technology - Biometrics - Technical requirements for face recognition system

---This is a DRAFT version for illustration, not a final translation. Full copy of true-PDF in English version (including equations, symbols, images, flow-chart, tables, and figures etc.) will be manually/carefully translated upon your order.
Information technology -- Biometrics -- Technical requirements for face recognition system ICS 35.240.15 CCSL67 National Standards of People's Republic of China information technology biometrics Technical Requirements for Face Recognition System recognition system 2023-05-01 implementation State Administration for Market Regulation Released by the National Standardization Management Committee

Preface III 1 Scope 1 2 Normative references 1 3 Terms and Definitions 1 4 Abbreviations 3 5 System architecture and business process 3 5.1 System Architecture 3 5.2 Business process 4 6 Functional requirements 5 6.1 View acquisition 5 6.2 View Resolution 5 6.3 Storage 5 6.4 Comparison 6 6.5 Decision 6 6.6 Management 6 6.7 Application Open Interface 6 7 Performance requirements 7 7.1 Sample quality 7 7.2 Face detection 8 7.3 Face Registration 8 7.4 Face recognition 8 7.5 Response time 9 Appendix A (Normative) Liveness Detection 10 Reference 12

foreword

This document is in accordance with the provisions of GB/T 1.1-2020 "Guidelines for Standardization Work Part 1.Structure and Drafting Rules for Standardization Documents" drafting. Please note that some contents of this document may refer to patents. The issuing agency of this document assumes no responsibility for identifying patents. This document is proposed and managed by the National Information Technology Standardization Technical Committee (SAC/TC28). This document was drafted by. Shanghai SenseTime Intelligent Technology Co., Ltd., China Electronics Standardization Research Institute, and Yuncong Technology Group Co., Ltd. Co., Ltd., Huawei Technologies Co., Ltd., Beijing Eyes Intelligent Technology Co., Ltd., Hangzhou Hikvision Digital Technology Co., Ltd., HKUST News Fly Co., Ltd., Shanghai Yitu Network Technology Co., Ltd., Ant Technology Group Co., Ltd., Shenzhen Tencent Computer System Co., Ltd. Co., Ltd., Beijing Baidu Netcom Technology Co., Ltd., Jianxin Financial Technology Co., Ltd., Shenzhen Yuntian Lifei Technology Co., Ltd., the company Anbu Third Research Institute, Fuzhou Data Technology Research Institute Co., Ltd., Nuctech Technology Co., Ltd., ZTE Corporation, Beijing Jichuang North Technology Co., Ltd., Zhejiang Dahua Technology Co., Ltd., Lenovo Zhongtian Technology Co., Ltd., Qingdao Hisense Network Technology Co., Ltd., Jingdong Technology Holdings Co., Ltd., Beijing Jingdong Shangke Information Technology Co., Ltd., Xiamen Meiya Baikexin Information Co., Ltd., Hangzhou Yufan Intelligent Technology Co., Ltd., Luo Kejiahua Technology Group Co., Ltd., Beijing Shuguang Yitong Technology Co., Ltd. Co., Ltd., Beijing Pengsi Technology Co., Ltd., Xiamen Ruiwei Information Technology Co., Ltd., Zhongke Vision (Beijing) Technology Co., Ltd., Entropy Technology Co., Ltd., Xiamen Entropy Technology Co., Ltd., Beijing University of Posts and Telecommunications, Shenzhen Jieshun Technology Industrial Co., Ltd., Shanghai Metrology Testing Technology Research Institute, Guangdong Zhongke Zhenheng Information Technology Co., Ltd., China Automotive Engineering Research Institute Co., Ltd., Newland Digital Technology Co., Ltd. Technology Co., Ltd., Fujian Haijing Technology Development Co., Ltd., Beijing Qingwei Intelligent Technology Co., Ltd., Beijing Xiaomi Mobile Software Co., Ltd. Company, Shengdian Century Technology Co., Ltd., Huizhou University, Guangdong Jiulian Technology Co., Ltd., State Grid Blockchain Technology (Beijing) Co., Ltd. Division, National Industrial Information Security Development Research Center. The main drafters of this document. Jiang Hui, Zhong Chen, Song Fangfang, Wang Wenfeng, Song Jiwei, Wu Yichao, Li Jun, Meng Fanhui, Yang Chunlin, Liu Qianying, Wang Chunmao, Wu Ziyang, Zhao Chunhao, Lin Guanchen, Jiang Zengzeng, Zhu Shengxian, Liu Lijuan, Cheng Bing, Liu Yuexia, Fang Bin, Zhang Li, Jia Xia, Zhang Jinfang, Hao Jingsong, Li Jie, Lu Fanbing, Wang Qili, Zhang Xin, Chen Zifeng, Zheng Dong, Liao Qiang, Yu Xueping, Xie Peibo, He Yifan, Wang Jinqiao, Lin Xiaoqing, Chen Shukai, He Bin, Li Peipei, Tang Jian, Sun Rongrong, Yang Jingfeng, Lei Jianmei, Cai Chunshui, Huang Laiqing, Wang Bo, Zhu Yajun, Feng Yadong, Luo Zhongliang, He Qiang, Gong Qiong, Wang Dong, Zhu Qianqian, Liang Ding, Yan Junjie, Wang Zhifang, Li Wei. information technology biometrics Technical Requirements for Face Recognition System

1 Scope

This document gives the architecture and business process of the face recognition system, and specifies the functional requirements and performance requirements. This document is applicable to the design, development and application of face recognition systems.

2 Normative references

The contents of the following documents constitute the essential provisions of this document through normative references in the text. Among them, dated references For documents, only the version corresponding to the date is applicable to this document; for undated reference documents, the latest version (including all amendments) is applicable to this document. GB/T 28826.2 Information technology public biometric identification exchange format framework - Part 2.Biometric identification registration authority operating procedures

3 Terms and Definitions

The following terms and definitions apply to this document. 3.1 face recognition facerecognition The automatic identification of a natural person based on that individual's facial features. [Source. GB/T 38671-2020, 3.1.2, modified] 3.2 A system that confirms a specific natural person or the identity of a specific natural person through face recognition. 3.3 user < Face recognition system> Any natural person or organization that interacts with the face recognition system in any way. Note. When discussing users related to face recognition systems, special terms will be used. Such as. face feature collection subject, face data subject, face recognition system Owner, face recognition system operator, etc. In this document, it is necessary to judge the specific object of "user" according to the context. [Source. GB/T 5271.37-2021, 3.7.20, modified] 3.4 Face feature item facefeature Values or markers extracted from face samples for comparison. 3.5 Face sample facesample The analog representation or digital representation of the facial feature characteristics before the extraction of facial feature items. 3.6 face reference facereference Stored face samples or face feature items that belong to the user and are used as face comparison objects. Note. The selection and use of the face reference type is related to the application. For specific regulations, see GB/T 35273 and GB/T 40660. 3.7 Pointer to a face reference data record in the face reference database. 3.8 face probe faceprobe A collection of face samples or face feature items that are input into the algorithm and compared with the face reference. Note. The selection and use of the face probe type is related to the application. For specific regulations, see GB/T 35273 and GB/T 40660. 3.9 face verification faceverification Compare the generated face probe with the given stored face reference (1.1 comparison) to confirm whether the user is the claimed A face recognition application method for personal identity. [Source. GB/T 38671-2020, 3.1.4, modified] 3.10 face recognition faceidentification Compare the generated face probe with all the face references in the specified range stored (1.N comparison) to confirm the user A face recognition application of identity. [Source. GB/T 38671-2020, 3.1.5, modified] 3.11 Sample quality samplequality A measure of the fitness of a face sample that satisfies a specified condition for a face recognition application. Note. The specified conditions for face recognition applications involve several aspects, such as clarity, face size, etc. 3.12 quality judgment qualityjudgment The process of checking whether the quality of the collected samples meets the specified conditions of the face recognition application. 3.13 Similarity score similarityscore Alignment score that increases with increasing similarity. [Source. GB/T 5271.37-2021, 3.3.35] 3.14 false acceptance rate falseacceptancerate In face verification, the ratio of the number of false acceptances to the sum of the number of correct rejections and the number of false acceptances. Note. False acceptance rate is sometimes also referred to as false match rate, false recognition rate, false recognition rate, false positive rate, etc., and the appropriate term is selected according to the specific application, expressed as a percentage. 3.15 False rejection rate falserejectionrate In face verification, the ratio of the number of false rejections to the sum of the number of correct acceptances and the number of false rejections. Note. False rejection rate is sometimes called rejection rate, false negative rate, etc., and the appropriate term is selected according to the specific application, expressed as a percentage. 3.16 In face recognition, the ratio of the number of false acceptances to the sum of the number of correct rejections and the number of false acceptances. Note. False acceptance rate is sometimes also called false alarm rate, false alarm rate, false alarm rate, etc., expressed as a percentage. The specific composition of the face recognition system is as follows. a) View acquisition subsystem. used for the acquisition of face images or videos, including face acquisition equipment and the face acquisition process any subprocess. b) View analysis subsystem. used for face image and/or video processing, including face detection, quality judgment, feature extraction, face Tracking, attribute detection, liveness detection, etc. c) Storage subsystem. used for the storage of face registration data and real-time collected data, including. 1) Face registration database. used for storage of registration data; 2) Real-time collection database. used for storage of collected data. d) Comparison subsystem. the face probe is compared with one or more face references to obtain a similarity score, and the similarity score is passed to Input to the face recognition decision-making subsystem, including two modes. 1) Face verification mode. compare the face probe with the specified face reference (1.1 comparison), and output a similarity score; 2) Face recognition mode. compare the face probe with some or all of the face references (1.N comparison), and output multiple similar degree score, and sorted according to the similarity score. e) Decision-making subsystem. According to one or more similarity scores, it provides decision-making results for face recognition, including two modes. 1) Face verification mode. when the similarity score exceeds the specified threshold, the face reference and the face probe are matched; 2) Face recognition mode. When the similarity score exceeds the specified threshold, the corresponding face reference constitutes a match with the face probe. potential candidates. f) Management subsystem. manage the overall strategy, execution and application of the face recognition system, including but not limited to. 1) Set thresholds, such as sample quality thresholds, similarity thresholds, liveness detection thresholds, etc.; 2) Log management. log generation, query and export, etc.; 3) Rights management. set the operation rights of different roles, etc.; 4) Interface configuration. configure the view acquisition subsystem of the face recognition system, etc.; 5) User management. store or delete user's face reference and other registration information; 6) Other management. control the working environment and storage of non-biometric data, provide feedback to the user during or after view collection Information feed, interactive management with face recognition applications, etc. g) Application open interface. the interface between the face recognition system and the face recognition application, including the face registration interface, face verification interface, Face recognition interface, live detection interface, etc. 5.2 Business Process The business process of the face recognition system includes face registration, face recognition, face update and face logout, etc. a) Face Registration. 1) Start the face registration process; 2) According to the face registration strategy, collect user data, such as face images, videos and/or non-biometric data; 3) The view analysis subsystem analyzes the collected views, such as quality judgment and liveness detection, etc.; 4) Store the user's data record in the face registration database; 5) End the registration process and record the log. b) Face recognition. 1) Start the face recognition process; 2) Collect face images or videos; 3) Compare the face probe with one or more face references; 4) According to the system strategy and similarity score, provide decision-making results for face recognition; 5) Transmit the decision result to the face recognition application; 6) End the identification process and record the log. c) Face update. 1) Start the face update process; 2) According to the face update strategy, use the face reference that passed this comparison to replace or partially replace the previous face reference; 3) End the update process and record the log. d) Face cancellation. 1) Start the face logout process; 2) According to the face cancellation policy, delete or anonymize all data associated with the user to be canceled in the face registration database; 3) End the logout process and record the log.

6 Functional requirements

6.1 View Collection The face recognition system should have the function of face collection and output face images or videos, and should meet the following requirements. a) Automatically judge the location of the collection object, and automatically adjust the collection equipment according to the height and distance of the collection object; b) When the collection objects are people with different physical functions, functions such as voice prompts and/or font size adjustments are provided. 6.2 View Analysis The face recognition system should have the view analysis function and meet the following requirements. a) It should be able to perform face detection; b) It should be able to extract face feature items; c) The quality of the sample should be judged and the judgment result should be given, and the face sample that fails the quality judgment can be prompted to re-collect; d) It is advisable to carry out biopsy test on the samples and give the test results. The biopsy test should comply with the provisions of Appendix A. ISO /IEC 30107-2 The liveness detection data record format is given; e) According to application requirements, face targets can be tracked; f) Face attributes can be detected, such as gender, age, hairstyle, whether to wear a mask, whether to wear glasses, etc. Note. In practical applications, whether to use face samples that have not passed the quality judgment may depend on application requirements and system strategies. 6.3 Storage 6.3.1 The face recognition system should have the data storage function. When the face reference is stored in the face recognition system, the following requirements should be met. a) Support face reference storage to the face registration database, and return the face reference identifier, where the face registration complies with 6.3.4 Regulation; b) Each user's face reference corresponds to a unique face reference identifier; c) Support the deletion operation of the face reference identifier, after deletion, the identifier and the corresponding face reference become invalid; d) Support the face reference identifier query operation, and confirm whether the face reference identifier and the corresponding face reference are valid. 6.3.2 When the face reference is stored outside the face recognition system (such as a user token), the face recognition system should be able to obtain and use the face refer to. 6.3.3 For non-essential purposes, the face recognition system should not store real-time collection data. When the face recognition system has real-time data collection and storage function, the specific requirements are as follows. a) It should support the storage of face probes to the real-time acquisition database; b) The query and deletion of real-time collected data should be supported. 6.3.4 The face recognition system should support multiple registration methods and meet the following requirements. a) shall support on-site registration or off-site registration; b) It should support batch registration of face samples; c) It should meet the needs of a single user to register multiple face samples; d) It should support face registration of different image sources, such as real-time collected photos or stored photos, etc.; e) It can support face samples collected by one or more collection devices; f) During the registration process, it should be able to properly interact with the user, such as reminding the user to cooperate, prompting successful registration, etc. 6.4 Comparison The face recognition system should have a face comparison function and meet the following requirements. a) The obtained face feature items should be compared with the face feature items in the face registration database, and the similarity score should be calculated; b) The similarity score should be compared with the threshold, and the face comparison results with similarity scores higher than the threshold should be saved, which can support the comparison result. Results are retrieved and exported; c) It should have the function of judging and handling abnormal situations. Note 1.The similarity score is generally a real number ranging from 0 to 100.The lower the score, the less similar the facial features are, and the higher the score, the more similar the facial features. Note 2.Abnormal situations include but are not limited to comparison failure and export failure. 6.5 Decision making The face recognition system should have the face recognition decision-making function and meet the following requirements. a) Face verification. 1) If the similarity score compared with the face reference is greater than the specified threshold, it should be judged that the face probe matches the face reference, If it is lower than the specified threshold, it should be judged as a mismatch; 2) The similarity score and decision results should be output to the management subsystem. b) Face recognition. 1) If the similarity scores compared with all face references are less than the specified threshold, it should be judged that the face probe does not match any face reference matching; 2) If the similarity score compared with multiple face references is greater than the specified threshold, the face probe with the highest similarity score should be selected. Large face reference matching; 3) If it is necessary to output possible candidates, the length of the candidate list should be determined first, and then according to the similarity between the face probe and the face reference Degree score, sort output candidate list; 4) The similarity score and decision results should be output to the management subsystem. c) Before determining the result of the face recognition decision, the face recognition system should allow the user to try the number of times set in the ......

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