Interpretable Facial Relational Network Using Relational Importance
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Human face analysis is an important task in computer vision. According to cognitive-psychological studies, facial dynamics could provide crucial cues for face analysis. In particular, the motion of facial local regions in facial expression is related to the motion of other facial regions. In this paper, a novel deep learning approach which exploits the relations of facial local dynamics has been proposed to estimate facial traits from expression sequence. In order to exploit the relations of facial dynamics in local regions, the proposed network consists of a facial local dynamic feature encoding network and a facial relational network. The facial relational network is designed to be interpretable. Relational importance is automatically encoded and facial traits are estimated by combining relational features based on the relational importance. The relations of facial dynamics for facial trait estimation could be interpreted by using the relational importance. By comparative experiments, the effectiveness of the proposed method has been validated. Experimental results show that the proposed method outperforms the state-of-the-art methods in gender and age estimation.
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