Locality-Aware Channel-Wise Dropout for Occluded Face Recognition

doi:10.1109/TIP.2021.3132827

	Locality-Aware Channel-Wise Dropout for Occluded Face Recognition
	He, Mingjie 1,2; Zhang, Jie 1,2; Shan, Shiguang 1,2,3,4; Liu, Xiao 5; Wu, Zhongqin 5; Chen, Xilin 1,2
	2022
发表期刊	IEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN	1057-7149
卷号	31 页码:788-798
摘要	Face recognition remains a challenging task in unconstrained scenarios, especially when faces are partially occluded. To improve the robustness against occlusion, augmenting the training images with artificial occlusions has been proved as a useful approach. However, these artificial occlusions are commonly generated by adding a black rectangle or several object templates including sunglasses, scarfs and phones, which cannot well simulate the realistic occlusions. In this paper, based on the argument that the occlusion essentially damages a group of neurons, we propose a novel and elegant occlusion-simulation method via dropping the activations of a group of neurons in some elaborately selected channel. Specifically, we first employ a spatial regularization to encourage each feature channel to respond to local and different face regions. Then, the locality-aware channel-wise dropout (LCD) is designed to simulate occlusions by dropping out a few feature channels. The proposed LCD can encourage its succeeding layers to minimize the intra-class feature variance caused by occlusions, thus leading to improved robustness against occlusion. In addition, we design an auxiliary spatial attention module by learning a channel-wise attention vector to reweight the feature channels, which improves the contributions of non-occluded regions. Extensive experiments on various benchmarks show that the proposed method outperforms state-of-the-art methods with a remarkable improvement.
关键词	Face recognition Feature extraction Liquid crystal displays Robustness Neurons Image reconstruction Dictionaries Occluded face recognition locality-aware channel-wise dropout spatial attention module
DOI	10.1109/TIP.2021.3132827
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2017YFA0700800] ; National Natural Science Foundation of China[61806188] ; National Natural Science Foundation of China[61976219] ; Shanghai Municipal Science and Technology Major Project[2017SHZDZX01]
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000739998500001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：12[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/18372
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Shan, Shiguang
作者单位	1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China 3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China 4.Peng Cheng Lab, Shenzhen 518055, Peoples R China 5.Tomorrow Adv Life Educ Grp TAL, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	He, Mingjie,Zhang, Jie,Shan, Shiguang,et al. Locality-Aware Channel-Wise Dropout for Occluded Face Recognition[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2022,31:788-798.
APA	He, Mingjie,Zhang, Jie,Shan, Shiguang,Liu, Xiao,Wu, Zhongqin,&Chen, Xilin.(2022).Locality-Aware Channel-Wise Dropout for Occluded Face Recognition.IEEE TRANSACTIONS ON IMAGE PROCESSING,31,788-798.
MLA	He, Mingjie,et al."Locality-Aware Channel-Wise Dropout for Occluded Face Recognition".IEEE TRANSACTIONS ON IMAGE PROCESSING 31(2022):788-798.