Thermal to Visual Person Re-Identification Using Collaborative Metric Learning Based on Maximum Margin Matrix Factorization

Abstract

Thermal to visual person re-identification (T2V-ReID) is a cross-domain image retrieval problem. In this problem, the matching of a person’s image takes place, where the image is taken by different cameras (thermal and visual) at different times. This problem has numerous applications in night-time security surveillance. It is challenging due to the large intra-class variations and cross-domain discrepancies. Recently, deep metric learning methods are proposed for this problem. Still, there is a scope to improve the metric learning by generalizing the metric. In this paper, we have proposed the collaborative metric learning using Maximum Margin Matrix Factorization. It uses the group-wise similarities and collaboratively predicts the similarities. We can learn a more generalized metric by utilizing the maximized margin in this method. The proposed method is tested on the RegDB and RGB-D-T data sets, and the method outperforms the existing works in the few-shot learning settings.