Robust partial face recognition using instance-to-class distance
We present a new face recognition approach from partial face patches by using an instance-to-class distance. While numerous face recognition methods have been proposed over the past two decades, most of them recognize persons from whole face images. In many real world applications, partial faces usually occur in unconstrained scenarios such as visual surveillance systems. Hence, it is very important to recognize an arbitrary facial patch to enhance the intelligence of such systems. In this paper, we develop a robust partial face recognition approach based on local feature representation, where the similarity between each probe patch and gallery face is computed by using the instance-to-class distance with the sparse constraint. Experiments on two popular face datasets are presented to show the efficacy of our proposed method.