Learning sparse feature for eyeglasses problem in face recognition
Occlusion of eyeglasses, and strong specular reflections on eyeglasses (especially in near infrared (NIR) images), can deteriorate face recognition performance. In this paper, we present a novel method to overcome these problems. The proposed method applies the sparse representation (SR) technique in a local feature space so as to be more tolerant to mis-alignment and abnormal specular pixel values. The SR face features are further transformed by using discriminant analysis. These lead to a good balance between efficiency and robustness. Extensive experiments on a large NIR face database containing 292 persons with/without eyeglasses show the superiority of the proposed method compared with state-of-the-art methods.