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.

本文提出了基于von Mises-Fisher混合模型的深度学习方法，能有效地深度学习方向特征，实现了紧凑学习同一类别的实例，同时增加不同类别的实例之间的距离，融合了多种流行的loss函数，并在人脸验证任务中获得了当前最优结果。

基于von Mises-Fisher混合模型的深度学习：应用于人脸验证