Person re-identification by integrating metric learning and support vector machine

Abstract

Person re-identification (PRID) refers to a technology of matching person across non-overlapping camera views. Metric learning is one of the most commonly used methods in PRID. However, most of them do not explore the label information carried by the labeled training samples, which limits the improvement of recognition performance. To this end, we propose a joint learning method by integrating the discriminative metric learning, the support vector machine (SVM) and the identity discriminator into one model, so as to realize joint construction of metric learning and identity discriminator. In this process, the label information carried by the training samples is fully exploited and the latent identify space of pedestrians is constructed by predicting the person’s identity. To mitigate the appearance ambiguity caused by the variations in camera views, body poses, illumination and occlusion, we develop an extreme distance regularization term and introduce it into the joint learning framework to refine the solution spaces of the metric learning and discriminator. Finally, we present a similarity measure method by combining the advantages of the metric learning and the identity discriminator. Experimental results on several benchmark datasets show that the proposed method significantly outperforms some state-of-the-art methods.