Adaptively Weighted k-Tuple Metric Network for Kinship Verification
Huang, Sheng1,2; Lin, Jingkai2; Huangfu, Luwen3,4; Xing, Yun2; Hu, Junlin5; Zeng, Daniel Dajun6,7
Corresponding AuthorHuang, Sheng(
AbstractFacial 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
KeywordMeasurement Feature extraction Task analysis Faces Deep learning Convolutional neural networks Genetics Deep learning kinship verification metric learning relation network (RN) triplet loss
Indexed BySCI
Funding ProjectNational Natural Science Foundation of China[62176030] ; Natural Science Foundation of Chongqing[cstc2021jcyj-msxmX0568]
Funding OrganizationNational Natural Science Foundation of China ; Natural Science Foundation of Chongqing
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000785746000001
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Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorHuang, Sheng
Affiliation1.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
Recommended Citation
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.
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