Memory-Modulated Transformer Network for Heterogeneous Face Recognition

doi:10.1109/TIFS.2022.3177960

CORC > 自动化研究所 > 中国科学院自动化研究所 > 智能感知与计算研究中心

	Memory-Modulated Transformer Network for Heterogeneous Face Recognition
	Luo, Mandi 2,3,4; Wu, Haoxue 3,4; Huang, Huaibo 2,3,4; He, Weizan 1; He, Ran 2,3,4
刊名	IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY
	2022
卷号	17 页码:2095-2109
关键词	Face recognition Task analysis Transformers Encoding Feature extraction Image recognition Memory modules Heterogeneous face recognition style transformer memory network
ISSN号	1556-6013
DOI	10.1109/TIFS.2022.3177960
通讯作者	He, Ran(rhe@nlpria.ac.cn)
英文摘要	Heterogeneous face recognition (HFR) aims at matching face images across different domains. It is challenging due to the severe domain discrepancies and overfitting caused by small training datasets. Some researchers apply a "recognition via generation" strategy and propose to solve the problem by translating images from a given domain into the visual domain. However, in many HFR tasks such as near-infrablack HFR, there is no paiblack data, which makes it an unsupervised generation. Pose variations, background differences, and many other factors present challenges. Moreover, the generated results lack diversity since many previous works regard this image translation as a "one-to-one" generation task. Considering the information deficiency in the input images, we propose to formulate this image translation process as a "one-to-many" generation problem. Specifically, we introduce reference images to guide the generation process. We propose a memory module to explore the prototypical style patterns of the reference domain. After self-supervised updating, the memory items are attentively aggregated to represent the style information. Moreover, to subtly fuse the contents of input images with the style of reference images, we propose a novel style transformer module. Specifically, we crop the encoded input and reference feature maps into patches, and use the style transformer to establish long-range dependencies between the input and reference patches. Thus, the style of every input patch is transferblack based on those of the most relevant reference patches. Extensive experiments on multiple datasets for various HFR tasks, including NIR-VIS, thermal-VIS, sketch-photo, and gray-RGB, are conducted. The robustness and effectiveness of the proposed MMTN are demonstrated both quantitatively and qualitatively.
资助项目	National Natural Science Foundation of China[U21B2045] ; National Natural Science Foundation of China[U20A20223] ; Youth Innovation Promotion Association Chinese Academy of Sciences (CAS)[Y201929]
WOS关键词	SPECTRAL REGRESSION ; DICTIONARY ; RANKING
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000812529100004
资助机构	National Natural Science Foundation of China ; Youth Innovation Promotion Association Chinese Academy of Sciences (CAS)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/49231]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	He, Ran
作者单位	1.Beijing Inst Technol, Sch Automat, Beijing 100811, Peoples R China 2.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Ctr Res Intelligent Percept & Comp, Inst Automat, Natl Lab Pattern Recognit, Beijing 100049, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China
推荐引用方式 GB/T 7714	Luo, Mandi,Wu, Haoxue,Huang, Huaibo,et al. Memory-Modulated Transformer Network for Heterogeneous Face Recognition[J]. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,2022,17:2095-2109.
APA	Luo, Mandi,Wu, Haoxue,Huang, Huaibo,He, Weizan,&He, Ran.(2022).Memory-Modulated Transformer Network for Heterogeneous Face Recognition.IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY,17,2095-2109.
MLA	Luo, Mandi,et al."Memory-Modulated Transformer Network for Heterogeneous Face Recognition".IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 17(2022):2095-2109.