Attention Modeling for Face Recognition via Deep Learning

Abstract

Face recognition is an important area of research in cognitive science and machine learning. This is the first paper utilizing deep learning techniques to model human’s attention for face recognition. In our attention model based on bilinear deep belief network (DBDN), the discriminant information is maximized in a frame of simulating the human visual cortex and human’s perception. Comparative experiments demonstrate that from recognition accuracy our deep learning model outperforms both representative benchmark models and existing bio-inspired models. Furthermore, our model is able to automatically abstract and emphasize the important facial features and patterns which are consistent with the human’s attention map.


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