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- Title
Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition.
- Authors
Li, Suli; Lee, Hyo Jong
- Abstract
Deep-learning-based, cross-age face recognition has improved significantly in recent years. However, when using the discriminative method, it is still challenging to extract robust age-invariant features that can reduce the interference caused by age. In this paper, we propose a novel, effective, attention-based feature decomposition model, the age-invariant features extraction network, which can learn more discriminative feature representations and reduce the disturbance caused by aging. Our method uses an efficient channel attention block-based feature decomposition module to extract age-independent identity features from facial representations. Our end-to-end framework learns the age-invariant features directly, which is more convenient and can greatly reduce training complexity compared with existing multi-stage training methods. In addition, we propose a direct sum loss function to reduce the interference of age-related features. Our method achieves a comparable and stable performance. Experimental results demonstrate superior performance on four benchmarked datasets over the state-of-the-art. We obtain the relative improvements of 0.06%, 0.2%, and 2.2% on the cross-age datasets CACD-VS, AgeDB, and CALFW, respectively, and a relative 0.03% improvement on a general dataset LFW.
- Subjects
FACE perception; FEATURE extraction; CONVOLUTIONAL neural networks
- Publication
Applied Sciences (2076-3417), 2022, Vol 12, Issue 10, p4816
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app12104816