Unconstrained Face Recognition Using Infrared Images

Recently, face recognition (FR) has become an important research topic due to increase in video surveillance. However, the surveillance images may have vague non-frontal faces, especially with the unidentifiable face pose or unconstrained environment such as bad illumination and dark environment. As a result, most FR algorithms would not show good performance when they are applied on these images. On the contrary, it is common at surveillance field that only Single Sample per Person (SSPP) is available for identification. In order to resolve such issues, visible spectrum infrared images were used which can work in entirely dark condition without having any light variations. Furthermore, to effectively improve FR for both the low-quality SSPP and unidentifiable pose problem, an approach to synthesize 3D face modeling and pose variations is proposed in this paper. A 2D frontal face image is used to generate a 3D face model. Then several virtual face test images with different poses are synthesized from this model. A well-known Surveillance Camera’s Face (SCface) database is utilized to evaluate the proposed algorithm by using PCA, LDA, KPCA, KFA, RSLDA, LRPP-GRR, deep KNN and DLIB deep learning. The effectiveness of the proposed method is verified through simulations, where increase in average recognition rates up to 10%, 27.69%, 14.62%, 25.38%, 57.46%, 57.43, 37.69% and 63.28%, respectively, for SCface database as observed.