Dual Modal Face Recognition Based on Visible Light and Thermal Infrared

Abstract In recent years, face recognition has received widespread attention, in the use of visible light for face recognition, there are a series of problems that require auxiliary light sources, may be attacked by photos, and camouflage is difficult to penetrate. Compared with visible light recognition, thermal infrared face recognition shows usability, and it can still be stable in low light or dark extreme environmental conditions, but due to low resolution, it can not capture rich face feature information. In view of this paper proposes that dual mode face recognition can effectively integrate two single mode information and make up for the deficiency of single feature. Using a Bimodal Face network model based on visible light and thermal infrared, the idea of feature decoupling is introduced into image fusion. The cross-modal information is decomposed into common information and unique information, and the common information and unique information of different modes are fused respectively according to the feature fusion method. It is tested on the self-built dual modal dataset that this fusion strategy can achieve the optimal effect, and the accuracy rate can reach 97.08%, which is better than other five face recognition network models.