RVFace: Reliable Vector Guided Softmax Loss for Face Recognition

doi:10.1109/TIP.2022.3154293

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 生物识别与安全技术

	RVFace: Reliable Vector Guided Softmax Loss for Face Recognition
	Wang, Xiaobo 1,2,3; Wang, Shuo 4; Liang, Yanyan 3; Gu, Liang 1; Lei, Zhen2,5,6
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
	2022
卷号	31 页码:2337-2351
通讯作者	Liang, Yanyan(yyliang@must.edu.mo)
摘要	Face recognition has witnessed significant progress with the advances of deep convolutional neural networks (CNNs), and the central task of which is how to improve the feature discrimination. To this end, several margin-based (e.g., angular, additive and additive angular margins) softmax loss functions have been proposed to increase the feature margin between different classes. However, despite great achievements have been made, they mainly suffer from four issues: 1) They are based on the assumption of well-cleaned training sets, without considering the consequence of noisy labels inherently existing in most of face recognition datasets; 2) They ignore the importance of informative (e.g., semi-hard) features mining for discriminative learning; 3) They encourage the feature margin only from the perspective of ground truth class, without realizing the discriminability from other non-ground truth classes; and 4) They set the feature margin between different classes to be same and fixed, which may not adapt the situation of unbalanced data in different classes very well. To cope with these issues, this paper develops a novel loss function, which explicitly estimates the noisy labels to drop them and adaptively emphasizes the semi-hard feature vectors from the remaining reliable ones to guide the discriminative feature learning. Thus we can address all the above issues and achieve more discriminative features for face recognition. 'lb the best of our knowledge, this is the first attempt to inherit the advantages of feature-based noisy labels detection, feature mining and feature margin into a unified loss function. Extensive experimental results on a variety of face recognition benchmarks have demonstrated the effectiveness of our method over state-of-the-art alternatives. Our source code is available at http://www.cbsr.ia.ac.cn/users/xiaobowang/.
关键词	Deep face recognition noisy labels detection margin-based softmax loss mining-based softmax loss discriminative feature learning
DOI	10.1109/TIP.2022.3154293
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program[2020YFC2003901] ; Chinese National Natural Science Foundation[62106264] ; Chinese National Natural Science Foundation[61876178] ; Chinese National Natural Science Foundation[61976229] ; Chinese National Natural Science Foundation[62176256] ; Science and Technology Development Fund of Macau[0008/2019/A1] ; Science and Technology Development Fund of Macau[0010/2019/AFJ] ; Science and Technology Development Fund of Macau[0025/2019/AKP] ; Science and Technology Development Fund of Macau[0004/2020/A1] ; Science and Technology Development Fund of Macau[0070/2021/AMJ] ; Guangdong Provincial Key Research and Development Program[2019B010148001]
项目资助者	National Key Research and Development Program ; Chinese National Natural Science Foundation ; Science and Technology Development Fund of Macau ; Guangdong Provincial Key Research and Development Program
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000769973200002
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
七大方向——子方向分类	生物特征识别
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/48094
专题	多模态人工智能系统全国重点实验室_生物识别与安全技术
通讯作者	Liang, Yanyan
作者单位	1.Sangfor Technol Inc, Shenzhen 518052, Peoples R China 2.Chinese Acad Sci CASIA, Inst Automat, Ctr Biometr & Secur Res CBSR, Natl Lab Pattern Recognit NLPR, Beijing 100190, Peoples R China 3.Macau Univ Sci & Technol, Fac Informat Technol, Taipa, Macau, Peoples R China 4.Chinese Acad Sci CASIA, Inst Automat, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci UCAS, Sch Artificial Intelligence, Beijing 100049, Peoples R China 6.Chinese Acad Sci, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot, Hong Kong, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Wang, Xiaobo,Wang, Shuo,Liang, Yanyan,et al. RVFace: Reliable Vector Guided Softmax Loss for Face Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2022,31:2337-2351.
APA	Wang, Xiaobo,Wang, Shuo,Liang, Yanyan,Gu, Liang,&Lei, Zhen.(2022).RVFace: Reliable Vector Guided Softmax Loss for Face Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,31,2337-2351.
MLA	Wang, Xiaobo,et al."RVFace: Reliable Vector Guided Softmax Loss for Face Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 31(2022):2337-2351.