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MAM: Multiple Attention Mechanism Neural Networks for Cross-Age Face Recognition

Cross-age face recognition problem is of great challenge in practical applications because face features of the same person at different ages contain variant aging features in addition to the invariant identity features. To better extract the age-invariant identity features hiding beneath the age-variant aging features, a deep learning-based approach with multiple attention mechanisms is proposed in this paper. First, we propose the stepped local pooling strategy to improve the SE module. Then by incorporating the residual-attention mechanism, the self-attention mechanism, and the improved channel-attention mechanism to the backbone network, we proposed the Multiple Attention Mechanism Network (MAM-CNN) framework for the cross-age face recognition problem. The proposed framework can focus on essential face regions to highlight identity features and diminish the distractions caused by aging features. Experiments are carried out on two well-known public domain face aging datasets (MORPH and CACD-VS). The results yielded prove that the introduced multiple mechanisms jointly enhance the model performance by 0.96% and 0.52%, respectively, over the state-of-the-art algorithms.