Cross-view kernel similarity metric learning using pairwise constraints for person re-identification
Person re-identification is the task of matching pedestrian images across non-overlapping cameras. In this paper, we propose an efficient kernel based similarity metric learning for learning non-linear features using small scale training data for practical person re-ID systems. The method employs non-linear mappings combined with cross-view discriminative subspace learning and cross-view distance metric learning based on pairwise similarity constraints. It is a natural extension of Cross-view Quadratic Discriminant Analysis (XQDA) from linear to non-linear model using kernels. In addition to outperforming XQDA, the proposed method is computationally very efficient compared to its baselines. Extensive experiments on four benchmark datasets show that our method attains competitive performance against state-of-the-art methods. Our code is available at https://github.com/ferozalitm/Efficient-Kernel-XQDA.