Effective Attention-Based Feature Decomposition for Cross-Age Face Recognition
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Face recognition and analysis · Facial Rejuvenation and Surgery Techniques · Biometric Identification and Security
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science and Engineering, CAIIT, Jeonbuk National University, Jeonju 54896, Korea
Department of Computer Science and Engineering, Cangzhou Normal University, Cangzhou 061000, China
Funding
Ministry of Education
References (55)
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