Deep feature learning with mixed distance maximization for person Re-identification

Abstract

In despite of several advanced approaches, deep learning for person re-identification is still regarded as a challenging task due to local optima in the objective function of the deep networks in addition to various changes of poses and viewpoints etc. We introduced a novel neural network learning, or deep feature learning with mixed distance maximization, to solve the local optima problem in person re-identification. A local objective function is first defined to maximize the intra-distance among a triplet for person re-identification. Also, a global objective function is proposed to consider distances among triplets. Based on two objective functions, a main objective function is introduced to make full use of the information of triplets. This main objective function is defined based on the combination of two distances, called a mixed distance. This mixed distance can prevent that the triplets become close to each other and the matched pairs in each triplets become apart in deep learning with the relative distance comparison. We test our deep method with the mixed distance maximization on several datasets. Experimental results demonstrate that deep feature learning with the mixed distance maximization have promising discriminative capability in comparison with other ones.