Joint identification-verification for person re-identification: A four stream deep learning approach with improved quartet loss function

摘要

A deep four-stream convolutional neural network (CNN) is proposed for person re-identification (re-ID) to overcome the poor generalisation of the traditional triplet loss function. Specifically, the proposed method is a four-stream network, taking four input images where two images are from the same identity and the other two are from different identities. The network uses dual identification and verification losses in a single framework to minimise the intra-class distance while maximising the inter-class distance. Extensive experiments illustrate the state-of-the-art performance of the proposed approach on seven challenging person re-ID datasets: VIPeR, CUHK03, CUHK01, PRID2011, i-LIDS, Market-1501, and DukeMTMC-relD. In addition, we build a five-stream network and a four-stream network with an alternate formulation of positive and negative pairs to further explore the performance of the proposed four-stream network. We also demonstrate promising performance when training and testing sets are from different domains, highlighting the real-world applicability of the approach.