Paper Info
Title
Age-Invariant Face Recognition by Multi-Feature Fusionand Decomposition with Self-attention
Abstract
AbstractDifferent 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, extracted 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.
Year
DOI
Venue
2022
10.1145/3472810
ACM Transactions on Multimedia Computing, Communications, and Applications
Keywords
DocType
Volume
Age-invariant face recognition, feature fusion, feature decomposition, self-attention
Journal
18
Issue
ISSN
Citations 
1s
1551-6857
2
PageRank 
References 
Authors
0.38
37
9
Name
Order
Citations
PageRank
Chenggang Yan141032.87
Lixuan Meng220.72
Liang Li334224.75
Jiehua Zhang420.38
Zhan Wang521.06
Jian Yin61784.87
Jiyong Zhang715621.11
Yaoqi Sun840.74
Bolun Zheng9123.88