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Age-Invariant Face Recognition by Multi-Feature Fusion and Decomposition with Self-attention

文献类型：期刊论文

作者Yan, Chenggang2; Meng, Lixuan3; Li, Liang1; Zhang, Jiehua2; Wang, Zhan4; Yin, Jian3; Zhang, Jiyong2; Sun, Yaoqi2; Zheng, Bolun2
刊名ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS
出版日期2022-02-01
卷号18期号:1页码:18
ISSN号1551-6857
关键词Age-invariant face recognition feature fusion feature decomposition self-attention
DOI10.1145/3472810
英文摘要Different from general face recognition, age-invariant face recognition (AIFR) aims at matching faces with a big age gap. Previous discriminative methods usually focus on decomposing facial feature into age-related and age-invariant components, which suffer from the loss of facial identity information. In this article, we propose a novel Multi-feature Fusion and Decomposition (MFD) framework for age-invariant face recognition, which learns more discriminative and robust features and reduces the intra-class variants. Specifically, we first sample multiple face images of different ages with the same identity as a face time sequence. Then, the multi-head attention is employed to capture contextual information from facial feature series, extraded by the backbone network. Next, we combine feature decomposition with fusion based on the face time sequence to ensure that the final age-independent features effectively represent the identity information of the face and have stronger robustness against the aging process. Besides, we also mitigate imbalanced age distribution in the training data by a re-weighted age loss. We experimented with the proposed MFD over the popular CACD and CACD-VS datasets, where we show that our approach improves the AIFR performance than previous state-of-the-art methods. We simultaneously show the performance of MFD on LFW dataset.
资助项目National Key Research and Development Program of China[2020YFB1406604] ; National Natural Science Foundation of China[61931008] ; National Natural Science Foundation of China[61671196] ; National Natural Science Foundation of China[62071415] ; National Natural Science Foundation of China[62001146] ; National Natural Science Foundation of China[61701149] ; National Natural Science Foundation of China[61801157] ; National Natural Science Foundation of China[61971268] ; National Natural Science Foundation of China[61901145] ; National Natural Science Foundation of China[61901150] ; National Natural Science Foundation of China[61972123] ; National Natural Science Foundation of China[61771457] ; National Natural Science Foundation of China[61732007] ; Zhejiang Province Natural Science Foundation of China[LR17F030006] ; Zhejiang Province Natural Science Foundation of China[Q19F010030] ; Youth Innovation Promotion Association of Chinese Academy of Sciences[2020108] ; 111 Project[D17019]
WOS研究方向Computer Science
语种英语
出版者ASSOC COMPUTING MACHINERY
WOS记录号WOS:000772639300006
源URL[http://119.78.100.204/handle/2XEOYT63/18924]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Liang; Zhang, Jiehua; Zheng, Bolun
作者单位1.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing, Peoples R China
2.Hangzhou Dianzi Univ, 280 Xuelin Rd, Hangzhou, Zhejiang, Peoples R China
3.Shandong Univ, 180 Wenhua Western Rd, Weihai, Shandong, Peoples R China
4.Moreal Pte Ltd, 20 LORONG 35 GEYLANG,03-08, Singapore, Singapore
推荐引用方式
GB/T 7714		 Yan, Chenggang,Meng, Lixuan,Li, Liang,et al. Age-Invariant Face Recognition by Multi-Feature Fusion and Decomposition with Self-attention[J]. ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,2022,18(1):18.
APA Yan, Chenggang.,Meng, Lixuan.,Li, Liang.,Zhang, Jiehua.,Wang, Zhan.,...&Zheng, Bolun.(2022).Age-Invariant Face Recognition by Multi-Feature Fusion and Decomposition with Self-attention.ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS,18(1),18.
MLA Yan, Chenggang,et al."Age-Invariant Face Recognition by Multi-Feature Fusion and Decomposition with Self-attention".ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS 18.1(2022):18.

入库方式： OAI收割

来源：计算技术研究所

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