Near Infrared Visible

Near-infrared (NIR) and visible (VIS) image matching focuses on bridging the significant discrepancies between these spectral domains for applications like face recognition and person re-identification. Current research emphasizes developing robust algorithms, often employing deep learning architectures like Siamese networks and incorporating techniques such as frequency domain analysis and multimodal fusion to improve accuracy and reduce domain gaps. This work is crucial for advancing security and surveillance technologies, particularly in challenging low-light or adverse weather conditions where NIR imaging offers advantages. The development of more efficient and accurate NIR-VIS matching methods has significant implications for various fields, including biometrics and human-computer interaction.