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中国工业与应用数学学会会刊
主管:中华人民共和国教育部
主办:西安交通大学
ISSN 1005-3085  CN 61-1269/O1

工程数学学报 ›› 2021, Vol. 38 ›› Issue (4): 451-469.doi: 10.3969/j.issn.1005-3085.2021.04.001

• •    下一篇

基于深度学习的人脸识别方法综述

余璀璨,   李慧斌   

  1. 西安交通大学数学与统计学院 大数据算法与分析技术国家工程实验室,西安  710049
  • 出版日期:2021-08-15 发布日期:2021-10-15
  • 通讯作者: 李慧斌 E-mail: huibinli@xjtu.edu.cn
  • 基金资助:
    国家自然科学基金 (61976173);国家重点研发计划 (2018AAA0102201);教育部-中国移动人工智能建设资助项目 (MCM20190701);中央高校基本科研业务费 (xzy012019041);陕西省自然科学基础研究计划 (2019JQ-628).

Deep Learning Based 2D Face Recognition: a Survey

YU Cui-can,   LI Hui-bin   

  1. National Engineering Laboratory for Big Data Analytics, School of Mathematics and Statistical, Xi'an Jiaotong University, Xi'an 710049
  • Online:2021-08-15 Published:2021-10-15
  • Contact: H. Li. E-mail address: huibinli@xjtu.edu.cn
  • Supported by:
    The National Natural Science Foundation of China (61976173); the National Key Research and Development Program of China (2018AAA0102201); the Ministry of Education-CMCC Artificial Intelligence Construction Project (MCM20190701); the Fundamental Research Funds for the Central Universities (xzy012019041); the Natural Science Basic Research Plan in Shaanxi Province (2019JQ-628).

摘要: 人脸识别与虹膜识别、指纹识别、步态识别等其它生物特征识别技术相比,具有自然、便捷、用户体验友好等独特优势,因而受到了学术界和工业界的广泛关注.近年来,在深度学习技术的驱动下,人脸识别技术取得了突破性进展,在面对表情、姿态、光照、遮挡等外在干扰因素时,仍表现出较好的鲁棒性.特别地,基于深度学习的人脸识别技术已广泛应用于安防、金融、教育、交通、新零售等应用领域.我们认识到,在人脸识别技术不断走向大众化的过程中,急需一些综述性的和普及性的文献来总结人脸识别技术的基本原理和基本方法.基于此,本文首先简要回顾了人脸识别的发展脉络,之后从人脸预处理、深度特征学习、特征比对、人脸数据集、评价标准五个方面重点介绍了基于深度学习的人脸识别技术.最后指出了人脸识别技术未来的发展趋势.

关键词: 人脸识别, 深度学习, 卷积神经网络, 特征学习

Abstract: Compared with iris, fingerprint, gait, and other biometric recognition technologies, face recognition has attracted wide attention from academia to industry due to its unique advantages such as natural, convenient, and user-friendly experience. In recent years, driven by deep learning technology, face recognition has made a breakthrough, which shows strong robustness even when suffering from obstacles like facial expression, head pose, illumination, and external occlusions. In particular, deep face recognition technologies have been widely used in security, finance, education, transportation, new retail, and other applications. We realise that in the process of deep face recognition technology becoming widespread, there is an urgent need for some review articles to summarise the basic principles and methods of deep face recognition. This paper first briefly reviews the development of face recognition and then introduces the deep learning based face recognition methods from five aspects: face preprocessing, deep feature learning, feature comparison, face datasets, and evaluation. Finally, the development trend of deep face recognition is discussed.

Key words: face recognition, deep learning, convolutional neural network, feature learning

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