An incremental learning face recognition system for single sample per person

Tao Zhu, Furao Shen, Jinxi Zhao

Abstract: Making recognition more reliable under the condition of single sample per person is a great challenge in computer vision. In this paper, we propose a subspace based face recognition system which focuses on dealing with this problem. Inspired by the Single Image Subspace (SIS) method and the concept of typical machine learning algorithms, we design an online incremental learning system which can keep learning information from input images to improve the system performance. By combining the strengths of principal angles based similarity measure, a threshold policy and a novel sample subspace updating algorithm, the task of robust face recognition is accomplished. Experimental results on AR and EYALE database are presented to demonstrate the effectiveness of the proposed method.