LAUSR

Single-sample face recognition has been investigated by a few researches over the past few decades. However, due to the demand of identity of interest searching from video surveillance in recent years, this system has been expanded to open-set face recognition (OSFR) scheme. The OSFR system provides the identity of registered subjects and rejects the unregistered ones using only single-sample reference. This is important in video surveillance applications in which both database members and non-members are expected to appear in the scene. In this paper, we propose to use fuzzy ARTMAP neural network to solve the problem of open-set single-sample face recognition in real-world video surveillance scenario. Our proposed approach can recognize faces in near-frontal views under various illumination and facial expression conditions. Facial features are extracted using histograms of oriented gradients and Gabor wavelets and then fused using canonical correlation analysis to yield feature vectors that are robust against the aforementioned conditions. The fuzzy ARTMAP classifier has been trained using only single sample per person. We have conducted experiments on three challenging benchmark datasets, namely AR, FRGC, and ChokePoint. The experimental results have shown that the proposed approach has a superior performance than the state-of-the-art approaches.