Biologically inspired lighting invariant facial identity recognition

Over the past decade, a considerable amount of literature has been published on face recognition. Since recognition of frontal face images under controlled settings has become easy to achieve, a number of recent studies have emphasized the importance of robustness to variations in pose and illumination. So in this paper, we undertake the task of recognizing face images taken under drastic lighting variations using a hierarchical facial identity recognition framework inspired by the human vision system. The proposed system employs a novel log polar-encoding of Gabor filtered outputs, in order to extract scale and rotation invariant edge information from face images. Besides the novel encoding strategy, an explicit pre-processing step is proposed to deal with drastic lighting changes. We tested the facial identity recognition framework on two popular face databases, Yale and ORL face database, and obtained recognition rates on par with recent works. Besides these standard databases, we tested on the Yale B database, which was specifically designed to test illumination invariance. In summary, we have outperformed state-of-the-art methods on the challenging Yale B face database using the proposed facial identity recognition framework.