Person re-identification by the asymmetric triplet and identification loss function

Person re-identification(re-id) aims to match the same individuals across different non-overlapping camera views. In this paper, we analyze the effectiveness of two widely used triplet loss and softmax loss on person re-id task. We conclude that the triplet loss function is suitable for the relatively small datasets with the shallow neural network, while the softmax loss works better on larger datasts with relatively deeper network architecture. Both of them are essential to the person re-id task. Moreover, we present a convolutional neural network (CNN) model under the joint supervision of the triplet loss and softmax loss for person re-id. This method can get a slightly better performance than either of them. The triplet loss makes the distance of the same individual's images closer, and pushes the instances of different individuals far apart from each other, which can effectively reduce the intra-personal variations. Meanwhile, the person identification cost, which is implemented by the softmax loss with the "center loss" embedded, can discriminatively learn some identity-related feature representations (i.e. features with large inter-personal variations). Extensive experimental results demonstrate the effectiveness of our proposed method, and we have obtained promising performance on the challenging i-LIDS, PRID2011 and CUHK03 datasets.