Thermal to Visual Face Recognition using Transfer Learning

Inter-modality face recognition refers to the matching of face images between different modalities and is done usually by taking visual images as source and one of the other modalities as a target. Performing facial recognition between thermal to visual is a tough task because of nonlinear spectral characteristics of thermal and visual images. However, this is a desirable requirement for night-time security applications and military surveillance. In this paper, we propose a method to improve the thermal classifier accuracy by using transfer learning and as a result, the accuracy of thermal to visual face recognition gets increased. The proposed method is tested on RGB-D-T dataset (45900 images) and UND-Xl collection (4584 images). Experimental results show that the overall accuracy of thermal to visual face recognition by transferring the knowledge gets increased to 94.32% from 89.3% on RGB-D-T dataset and from 81.54% to 90.33% on UND-Xl dataset.