Two-Phase Feature Fusion Network For Visible-Infrared Person Re-Identification

Visible-infrared person re-identification(VI-ReID) is a challenging problem that aims to match pedestrians captured by visible and infrared cameras. Prevailing methods in this field mainly focus on learning sharable feature representations from the last layer of deep convolution neural networks(CNNs). However, due to the large intra-modality variations and cross-modality variations, the last layerƒ??s sharable feature representations are less discriminative. To remedy this, we propose a novel Two-Phase Feature Fusion Network(TFFN) to enhance the discriminative feature learning via feature fusion. Specifically, TFFN contains two fusion modules: (1) Multi-Level Fusion Module(MLFM) that re-weights and fuses intra-modality multi-level features to utilize high- and low-level information; (2) Graph-Level Fusion Module (GLFM) that mines and fuses graph-level rich mutual information across the two modalities to reduce the modality variations. Additionally, for effective fusion, we develop a deep supervision method to enhance the discrimination of pre-fusion features and eliminate noise information. Extensive experiments show that TFFN outperforms the state-of-the-art methods on two mainstream VI-ReID datasets: SYSU-MM01 and RegDB.