Synergizing CNNs and Transformers for Accurate Face Age Estimation

Estimating the actual and perceived age of human faces has garnered significant interest for its wide-ranging practical applications. Various intelligent scenarios stand to gain from these computational systems capable of accurately predicting individuals’ ages. Automated age estimation systems are particularly valuable in fields such as medical diagnostics, facial product development, casting for films, assessing the impact of cosmetic procedures, and anti-aging treatments. In the realm where deep networks have demonstrated their supremacy as the fron-trunners among machine learning tools, Our approach integrates Convolutional Neural Networks (CNN) with Transformers. This novel system enhances information extraction by utilizing transformer attention mechanisms, rather than solely depending on features extracted from convolutional neural networks for estimating age. Based on the experiments conducted, the system effectively captures the sequential progression and continuous nature of the aging process. Furthermore, the proposed model surpasses the cutting-edge model by delivering exceptional results, achieving the lowest mean absolute errors of 2.31 for MORPH II, 5.35 for CACD, and 2.91 for AFAD.