Abstract
This paper presents a cross-age facial recognition model that integrates Convolutional Neural Networks (CNN) with Transformers. The model first utilizes a depth-separable T2T-ViT network to extract rich facial features. Subsequently, it employs a multi-scale attention decomposition module to nonlinearly decouple age and identity features. The feature decomposition is jointly constrained by mutual information minimization, cross-entropy, and the Arcface function. The model achieves accuracy rates of 94.97%, 99.51%, and 95.81% on three benchmark datasets: FG-NET, CACD_VS, and CALFW, respectively, matching or surpassing the state-of-the-art (SOTA) performance. These results indicate that the proposed model can extract robust facial information and efficiently decouple features, achieving advanced recognition performance.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have