Enhancing Face Recognition With Detachable Self-Supervised Bypass Networks

doi:10.1109/TIP.2024.3364067

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	Enhancing Face Recognition With Detachable Self-Supervised Bypass Networks
	He, Mingjie 1,2; Zhang, Jie 1,2,3; Shan, Shiguang 1,2; Chen, Xilin 1,2
	2024
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	33 页码:1588-1599
摘要	Attributed to the development of deep networks and abundant data, automatic face recognition (FR) has quickly reached human-level capacity in the past few years. However, the FR problem is not perfectly solved in case of large poses and uncontrolled occlusions. In this paper, we propose a novel bypass enhanced representation learning (BERL) method to improve face recognition under unconstrained scenarios. The proposed method integrates self-supervised learning and supervised learning together by attaching two auxiliary bypasses, a 3D reconstruction bypass and a blind inpainting bypass, to assist robust feature learning for face recognition. Among them, the 3D reconstruction bypass enforces the face recognition network to encode pose independent 3D facial information, which enhances the robustness to various poses. The blind inpainting bypass enforces the face recognition network to capture more facial context information for face inpainting, which enhances the robustness to occlusions. The whole framework is trained in end-to-end manner with two self-supervised tasks above and the classic supervised face identification task. During inference, the two auxiliary bypasses can be detached from the face recognition network, avoiding any additional computational overhead. Extensive experimental results on various face recognition benchmarks show that, without any cost of extra annotations and computations, our method outperforms state-of-the-art methods. Moreover, the learnt representations can also well generalize to other face-related downstream tasks such as the facial attribute recognition with limited labeled data.
关键词	Face recognition Task analysis Three-dimensional displays Training Supervised learning Self-supervised learning Image reconstruction bypass enhanced representation learning 3D reconstruction bypass blind inpainting bypass
DOI	10.1109/TIP.2024.3364067
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:001177650300003
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38725
专题	中国科学院计算技术研究所
通讯作者	Shan, Shiguang
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc Chinese Acad Sci, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Intelligent Comp Technol, Suzhou 215000, Peoples R China
推荐引用方式 GB/T 7714	He, Mingjie,Zhang, Jie,Shan, Shiguang,et al. Enhancing Face Recognition With Detachable Self-Supervised Bypass Networks[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2024,33:1588-1599.
APA	He, Mingjie,Zhang, Jie,Shan, Shiguang,&Chen, Xilin.(2024).Enhancing Face Recognition With Detachable Self-Supervised Bypass Networks.IEEE TRANSACTIONS ON IMAGE PROCESSING,33,1588-1599.
MLA	He, Mingjie,et al."Enhancing Face Recognition With Detachable Self-Supervised Bypass Networks".IEEE TRANSACTIONS ON IMAGE PROCESSING 33(2024):1588-1599.