Learning Modality-Specific Representations for Visible-Infrared Person Re-Identification.

Traditional person re-identification (re-id) methods perform poorly under changing illuminations. This situation can be addressed by using dual-cameras that capture visible images in a bright environment and infrared images in a dark environment. Yet, this scheme needs to solve the visible-infrared matching issue, which is largely under-studied. Matching pedestrians across heterogeneous modalities is extremely challenging because of different visual characteristics. In this paper, we propose a novel framework that employ modality-specific networks to tackle with the heterogeneous matching problem. The proposed framework utilizes the modality-related information and extracts modality-specific representations (MSR) by constructing an individual network for each modality. In addition, a cross-modality Euclidean constraint is introduced to narrow the gap between different networks. We also integrate the modality-shared layers into modality-specific networks to extract shareable information and use a modality-shared identity loss to facilitate the extraction of modality-invariant features. Then a modality-specific discriminant metric is learned for each domain to strengthen the discriminative power of MSR. Eventually, we use a view classifier to learn view information. The experiments demonstrate that the MSR effectively improves the performance of deep networks on VI-REID and remarkably outperforms the state-of-the-art methods.