von Mises-Fisher Mixture Model-based Deep learning: Application to Face Verification
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A number of pattern recognition tasks, e.g., face verification, can be boiled down to classification or clustering of unit length directional feature vectors whose distance can be simply computed by their angle. In this paper, we propose the von Mises-Fisher (vMF) mixture model as the theoretical foundation for an effective deep-learning of such directional features and derive a novel vMF Mixture Loss and its corresponding vMF deep features. The proposed vMF features learning achieves a discriminative learning, i.e., compacting the instances of the same class while increasing the distance of instances from different classes, and subsumes a number of loss functions or deep learning practice, e.g., normalization. The experiments carried out on face verification using 4 different challenging face datasets, i.e., LFW, IJB-A, YouTube Faces and CACD, show the effectiveness of the proposed approach, which displays very competitive and state-of-the-art results.
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