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Visible-to-thermal face image matching is a challenging variate ofcross-modality recognition. The challenge lies in the large modality gap andlow correlation between visible and thermal modalities. Existing approachesemploy image preprocessing, feature extraction, or common subspace projection,which are independent problems in themselves. In this paper, we propose anend-to-end framework for cross-modal face recognition. The proposed algorithmaims to learn identity-discriminative features from unprocessed facial imagesand identify cross-modal image pairs. A novel Unit-Class Loss is proposed forpreserving identity information while discarding modality information. Inaddition, a Cross-Modality Discriminator block is proposed for integratingimage-pair classification capability into the network. The proposed network canbe used to extract modality-independent vector representations or amatching-pair classification for test images. Our cross-modality facerecognition experiments on five independent databases demonstrate that theproposed method achieves marked improvement over existing state-of-the-artmethods.

Heterogeneous Visible-Thermal and Visible-Infrared Face Recognition using Unit-Class Loss and Cross-Modality Discriminator