Local voting networks for human face recognition

We investigate a template matching-based classifier system which uses local distance computations and a voting scheme. The proposed system has a mechanism to reject unknown patterns, and provides invariance to small amounts of translation. Experimental results are presented for 3 different types of face recognition problems: classification experiments where we measure the ability of the system to identify known individuals; false positive experiments where we measure the ability of the system to reject images of unknown individuals; and temporal experiments, where we measure the ability of the system to recognize images of known individuals taken over a period of time (6 months). The results show that the proposed system performs well on all 3 of these problems.