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Adaptively Weighted k-Tuple Metric Network for Kinship Verification

文献类型：期刊论文

作者Huang, Sheng5,6; Lin, Jingkai5; Huangfu, Luwen1,4; Xing, Yun5; Hu, Junlin7; Zeng, Daniel Dajun2,3
刊名IEEE TRANSACTIONS ON CYBERNETICS
出版日期2022-04-19
页码14
ISSN号2168-2267
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
DOI10.1109/TCYB.2022.3163707
通讯作者Huang, Sheng(huangsheng@cqu.edu.cn)
英文摘要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
WOS关键词KIN RECOGNITION SIGNALS ; FACE ; DEEP ; DESCRIPTOR ; FEATURES ; SUBJECT
资助项目National Natural Science Foundation of China[62176030] ; Natural Science Foundation of Chongqing[cstc2021jcyj-msxmX0568]
WOS研究方向Automation & Control Systems ; Computer Science
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000785746000001
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Chongqing
源URL[http://ir.ia.ac.cn/handle/173211/48403]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心
通讯作者Huang, Sheng
作者单位1.San Diego State Univ, Ctr Human Dynam Mobile Age, San Diego, CA 92182 USA
2.Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100045, Peoples R China
4.San Diego State Univ, Fowler Coll Business, San Diego, CA 92182 USA
5.Chongqing Univ, Sch Big Data & Software Engn, Chongqing 400044, Peoples R China
6.Chongqing Univ, Key Lab Dependable Serv Comp Cyber Phys Soc, Minist Educ, Chongqing 400044, Peoples R China
7.Beihang Univ, Sch Software, Beijing 100191, 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.

入库方式： OAI收割

来源：自动化研究所

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