A New Attention-based Architecture for Robust Face-Age Recognition

Face age recognition task has become essential in several applications like minors security and marketing. However, despite the recent advances in deep learning algorithms, developing accurate and efficient architecture for face age classification remains difficult due to the unconstrained environment and variation on aging effect. Requiring both a distinctive and descriptive face age representation model. Requiring both a distinctive and descriptive face age representation models. We introduce, in this paper, a three-stage system to enhance face age recognition using the transformer architecture and pre-trained Convolutional Neural Networks (CNN). The system begins with extracting the age representation using Vision Transforms (ViT) and FaceNet model. Subsequently, the Pearson correlation coefficient is considered for selecting the face age pertinent features. Hence, a Multi-Head Attention (MHA) block is used to extract the final descriptive representation. Finally, the Support Vector Machine Classifier (SVM) is trained to distinguish between the different age classes. The challenging FG-NET dataset was considered in our work for evaluating the proposed architecture. The conducted experimental study shows evidence that our approach ranks top of the list of recent potential published work with an 63% recognition rate.