Adaptively Weighted k-Tuple Metric Network for Kinship Verification

doi:10.1109/TCYB.2022.3163707

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 互联网大数据与信息安全

	Adaptively Weighted k-Tuple Metric Network for Kinship Verification
	Huang, Sheng 1,2; Lin, Jingkai 2; Huangfu, Luwen 3,4; Xing, Yun 2; Hu, Junlin 5; Zeng, Daniel Dajun6,7
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
ISSN	2168-2267
	2022-04-19
页码	14
通讯作者	Huang, Sheng(huangsheng@cqu.edu.cn)
摘要	Facial image-based kinship verification is a rapidly growing field in computer vision and biometrics. The key to determining whether a pair of facial images has a kin relation is to train a model that can enlarge the margin between the faces that have no kin relation while reducing the distance between faces that have a kin relation. Most existing approaches primarily exploit duplet (i.e., two input samples without cross pair) or triplet (i.e., single negative pair for each positive pair with low-order cross pair) information, omitting discriminative features from multiple negative pairs. These approaches suffer from weak generalizability, resulting in unsatisfactory performance. Inspired by human visual systems that incorporate both low-order and high-order cross-pair information from local and global perspectives, we propose to leverage high-order cross-pair features and develop a novel end-to-end deep learning model called the adaptively weighted k-tuple metric network (AWk-TMN). Our main contributions are three-fold. First, a novel cross-pair metric learning loss based on k-tuplet loss is introduced. It naturally captures both the low-order and high-order discriminative features from multiple negative pairs. Second, an adaptively weighted scheme is formulated to better highlight hard negative examples among multiple negative pairs, leading to enhanced performance. Third, the model utilizes multiple levels of convolutional features and jointly optimizes feature and metric learning to further exploit the low-order and high-order representational power. Extensive experimental results on three popular kinship verification datasets demonstrate the effectiveness of our proposed AWk-TMN approach compared with several state-of-the-art approaches. The source codes and models are released.1
关键词	Measurement Feature extraction Task analysis Faces Deep learning Convolutional neural networks Genetics Deep learning kinship verification metric learning relation network (RN) triplet loss
DOI	10.1109/TCYB.2022.3163707
关键词[WOS]	KIN RECOGNITION SIGNALS ; FACE ; DEEP ; DESCRIPTOR ; FEATURES ; SUBJECT
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62176030] ; Natural Science Foundation of Chongqing[cstc2021jcyj-msxmX0568]
项目资助者	National Natural Science Foundation of China ; Natural Science Foundation of Chongqing
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000785746000001
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：4[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/173211/48404
专题	多模态人工智能系统全国重点实验室_互联网大数据与信息安全
通讯作者	Huang, Sheng
作者单位	1.Chongqing Univ, Key Lab Dependable Serv Comp Cyber Phys Soc, Minist Educ, Chongqing 400044, Peoples R China 2.Chongqing Univ, Sch Big Data & Software Engn, Chongqing 400044, Peoples R China 3.San Diego State Univ, Fowler Coll Business, San Diego, CA 92182 USA 4.San Diego State Univ, Ctr Human Dynam Mobile Age, San Diego, CA 92182 USA 5.Beihang Univ, Sch Software, Beijing 100191, Peoples R China 6.Chinese Acad Sci, Inst Automat, Beijing 100045, Peoples R China 7.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Huang, Sheng,Lin, Jingkai,Huangfu, Luwen,et al. Adaptively Weighted k-Tuple Metric Network for Kinship Verification[J]. IEEE TRANSACTIONS ON CYBERNETICS,2022:14.
APA	Huang, Sheng,Lin, Jingkai,Huangfu, Luwen,Xing, Yun,Hu, Junlin,&Zeng, Daniel Dajun.(2022).Adaptively Weighted k-Tuple Metric Network for Kinship Verification.IEEE TRANSACTIONS ON CYBERNETICS,14.
MLA	Huang, Sheng,et al."Adaptively Weighted k-Tuple Metric Network for Kinship Verification".IEEE TRANSACTIONS ON CYBERNETICS (2022):14.