Mutual Component Analysis for Heterogeneous Face Recognition

doi:10.1145/2807705

OPT OpenIR > 光谱成像技术研究室

	Mutual Component Analysis for Heterogeneous Face Recognition
	Li, Zhifeng 1; Gong, Dihong 1; Li, Qiang 2; Tao, Dacheng2 ; Li, Xuelong3
作者部门	光学影像学习与分析中心
	2016-04-01
发表期刊	ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
ISSN	2157-6904
卷号	7 期号:3
产权排序	3
摘要	Heterogeneous face recognition, also known as cross-modality face recognition or intermodality face recognition, refers to matching two face images from alternative image modalities. Since face images from different image modalities of the same person are associated with the same face object, there should be mutual components that reflect those intrinsic face characteristics that are invariant to the image modalities. Motivated by this rationality, we propose a novel approach called Mutual Component Analysis (MCA) to infer the mutual components for robust heterogeneous face recognition. In the MCA approach, a generative model is first proposed to model the process of generating face images in different modalities, and then an Expectation Maximization (EM) algorithm is designed to iteratively learn the model parameters. The learned generative model is able to infer the mutual components (which we call the hidden factor, where hidden means the factor is unreachable and invisible, and can only be inferred from observations) that are associated with the person's identity, thus enabling fast and effective matching for cross-modality face recognition. To enhance recognition performance, we propose an MCA-based multiclassifier framework using multiple local features. Experimental results show that our new approach significantly outperforms the state-of-the-art results on two typical application scenarios: sketch-to-photo and infrared-to-visible face recognition.
文章类型	Article
关键词	Algorithms Performance Face Recognition Heterogeneous Face Recognition Mutual Component Analysis (Mca)
学科领域	计算机应用其他学科（含图像处理）
WOS标题词	Science & Technology ; Technology
DOI	10.1145/2807705
收录类别	SCI ; EI
关键词[WOS]	DISCRIMINANT-ANALYSIS ; SPECTRAL REGRESSION ; SKETCH RECOGNITION ; CLASSIFICATION ; PERFORMANCE ; FRAMEWORK
语种	英语
WOS研究方向	Computer Science
项目资助者	National Natural Science Foundation of China(61103164 ; Natural Science Foundation of Guangdong Province(2014A030313688) ; Australian Research Council Projects(FT-130101457 ; Key Laboratory of Human-Machine Intelligence-Synergy Systems ; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences ; Guangdong Innovative Research Team Program(201001D0104648280) ; Key Research Program of the Chinese Academy of Sciences(KGZD-EW-T03) ; Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong(MMT-8115038) ; 61125106) ; LP-140100569)
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:000373911200003
引用统计	被引频次：53[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/27880
专题	光谱成像技术研究室
作者单位	1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Beijing 100864, Peoples R China 2.Univ Technol Sydney, Fac Engn & Informat Technol, Ctr Quantum Computat & Intelligent Syst, 81 Broadway, Ultimo, NSW 2007, Australia 3.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning OPTIMAL, Xian 710119, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Li, Zhifeng,Gong, Dihong,Li, Qiang,et al. Mutual Component Analysis for Heterogeneous Face Recognition[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2016,7(3).
APA	Li, Zhifeng,Gong, Dihong,Li, Qiang,Tao, Dacheng,&Li, Xuelong.(2016).Mutual Component Analysis for Heterogeneous Face Recognition.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,7(3).
MLA	Li, Zhifeng,et al."Mutual Component Analysis for Heterogeneous Face Recognition".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 7.3(2016).

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