Robust “On-the-Fly” person identification using Sparse Representation

On-the-fly person identification using face has been receiving more and more attention recently as it exhibits additional challenges that include uncontrolled poses, change in illumination and focus as compared to that of conventional face recognition. In this paper, we propose an efficient scheme that can be structured in two steps namely face detection and person identification to accurately address the challenges in “On-the-Fly” person identification. The proposed face detection scheme is based on the skin color region detection and a novel region based association scheme which is efficient and innovative in locating the evolving face in the video. We then, propose to carry out the person identification by employing Log-Gabor transform and Sparse Representation Classifier (SRC). Extensive experiments are carried out on the public available video surveillance `ChokePoint´ dataset leading to the highest observed closed-set identification rate of 91% with efficient computational speed of 27 frames/minute on normal desktop.