Cross-Age Contrastive Learning for Age-Invariant Face Recognition

• URL: http://arxiv.org/abs/2312.11195v2
• Date: Tue, 2 Jan 2024 19:58:09 GMT
• Title: Cross-Age Contrastive Learning for Age-Invariant Face Recognition
• Authors: Haoyi Wang, Victor Sanchez, Chang-Tsun Li
• Abstract summary: Cross-age facial images are typically challenging and expensive to collect. Images of the same subject at different ages are usually hard or even impossible to obtain. We propose a novel semi-supervised learning approach named Cross-Age Contrastive Learning (CACon)
• Score: 29.243096587091575
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cross-age facial images are typically challenging and expensive to collect, making noise-free age-oriented datasets relatively small compared to widely-used large-scale facial datasets. Additionally, in real scenarios, images of the same subject at different ages are usually hard or even impossible to obtain. Both of these factors lead to a lack of supervised data, which limits the versatility of supervised methods for age-invariant face recognition, a critical task in applications such as security and biometrics. To address this issue, we propose a novel semi-supervised learning approach named Cross-Age Contrastive Learning (CACon). Thanks to the identity-preserving power of recent face synthesis models, CACon introduces a new contrastive learning method that leverages an additional synthesized sample from the input image. We also propose a new loss function in association with CACon to perform contrastive learning on a triplet of samples. We demonstrate that our method not only achieves state-of-the-art performance in homogeneous-dataset experiments on several age-invariant face recognition benchmarks but also outperforms other methods by a large margin in cross-dataset experiments.

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