Multi-feature based automatic face identification on kernel eigen spaces (KES) under unstable lighting conditions

Programmed Face Identification (PFI) of images more reliable even under unstable lighting conditions is one of the most important challenges for practical face recognition systems. We tackle this by combining the strengths of robust illumination normalization, local texture-based face representations, distance transform based matching, multiple feature fusion. Additionally we propose Phase Congruency Features which is an approach for detects points of order in the phase spectrum within images. By combining the results produced from both the above mentioned approaches, we try to increase the accuracy of recognizing the given image from group images even with difficult lighting conditions.