Pursuing face identity from view-specific representation to view-invariant representation
How to learn view-invariant facial representations is an important task for view-invariant face recognition. The recent work [1] discovered that the brain of the macaque monkey has a face-processing network, where some neurons are view-specific. Motivated by this discovery, this paper proposes a deep convolutional learning model for face recognition, which explicitly enforces this view-specific mechanism for learning view-invariant facial representations. The proposed model consists of two concatenated modules: the first one is a convolutional neural network (CNN) for learning the corresponding viewing pose to the input face image; the second one consists of multiple CNNs, each of which learns the corresponding frontal image of an image under a specific viewing pose. This method is of low computational cost, and it can be well trained with a relatively small number of samples. The experimental results on the MultiPIE dataset demonstrate the effectiveness of our proposed convolutional model in contrast to three state-of-the-art works.