Cross-Age Contrastive Learning for Age-Invariant Face Recognition
This work addresses age-invariant face recognition for security and biometrics applications, offering a novel method to overcome data limitations, though it is incremental in leveraging existing synthesis models.
The paper tackles the problem of age-invariant face recognition by proposing a semi-supervised approach called Cross-Age Contrastive Learning (CACon), which uses synthesized facial images to address data scarcity and achieves state-of-the-art performance on benchmarks with significant improvements in cross-dataset experiments.
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.