Automatic pose normalization for open-set single-sample face recognition in video surveillance

Face images acquired by video surveillance cameras usually involve large pose variations which significantly degrade the performance of face recognition systems. Existing techniques address the pose variation problem by normalizing the arbitrary poses to the desired pose prior to recognition. However, these methods may require 2D or 3D model fitting and manual facial landmarks annotation. In this work, we present an automatic pose normalization technique that is free from model fitting and manual intervention. Our method utilizes an automatic facial landmark detection algorithm and thin-plate splines warping method to normalize pose-varied face images to a canonical frontal pose. Detecting facial landmarks automatically in face images provides 2D surface points that are used by thin-plate splines warping to geometrically transform face images to the desired pose. Experimental results carried out on the FERET database have shown that the proposed method achieved a comparable or higher performance compared to the state-of-the-art pose normalization approaches under constrained conditions. Moreover, extended experiments on the ChokePoint database have shown that our method substantially improved the performance of our open-set single-sample face recognition approach in the surveillance environment.