Symmetry-Aided Frontal View Synthesis for Pose-Robust Face Recognition

This paper tackles the problem of pose variations in a 2D face recognition scenario. Using a training set of sparse face meshes, we built a point distribution model and identified the parameters which are responsible for controlling the apparent changes in shape due to turning and nodding the head, namely the pose parameters. Given a test image and its associated mesh, the pose parameters are set to typical values of frontal faces, thus obtaining a virtual frontal mesh. Taking advantage of facial symmetry, we overcome problems due to self-occlusion and virtual frontal faces are synthesized via thin plate splines-based texture mapping. These corrected images are then fed into a recognition system that makes use of Gabor filtering for feature extraction. The CMU PIE database is used to assess the performance of the proposed method in a closed-set identification scenario where large pose variations are present, achieving state-of-the-art results.