INFRARED-VISIBLE PERSON RE-IDENTIFICATION VIA CROSS-MODALITY BATCH NORMALIZED IDENTITY EMBEDDING AND MUTUAL LEARNING

Cross-Modality Infrared-Visible Person Re-identification (IV-REID) is an important application in intelligent video surveillance. Compared to traditional single-modality person re-identification (re-ID) task, IV-REID aims at matching pedestrian images across different spectrum camera views. In this work, we propose a simple but effective framework to reduce the modality discrepancy. First, a batch normalized cross-modality Identity (ID) embedding method is designed to ease the vanishing gradient problem in IV-REID. Second, we introduce a novel mutual learning strategy for different single-modality ID Embedding method to further learn discriminative representations under different modalities. Albeit simple, extensive experiments show that our method outperforms the state-of-the-art on RegDB and SYSU-MM01 datasets. Source code is publicly available at: https://github.com/linyq17/IV-REID