Near Infrared Face Recognition using End to End Light Convolution Neural Network

Face Recognition in the visible spectrum is a widely used biometric tool. However, it is often influenced by environmental lighting that reduces its performance drastically. So as to solve the effects of illumination, face images are captured in the near-infrared (NIR) spectrum. While several deep learning models are available to solve the problem of face recognition in the visible spectrum, only a few models exist in the NIR spectrum. This paper proposes an end to end light CNN architecture that performs face recognition in the NIR spectrum. The proposed architecture has given higher accuracies for CASIA NIR VIS 2.0 (98.16%) and Oulu CASIA (99.26%) which are considered to be the largest and the most challenging publicly available datasets. Further, it has produced high accuracies for other publicly available datasets namely PolyU (97.90%), CBSR (98.22%) and HITSZ (99.72%).