UMD Experiments with FRGC Data

Although significant work has been done in the field of face recognition, the performance of state-of-the art face recognition algorithms is not good enough to be effective in operational systems. Though most algorithms work well for controlled images, they are quite susceptible to changes in illumination and pose. Face Recognition Grand Challenge (FRGC) is an effort to examine such issues to suitably guide future research in the area. This paper describes the efforts made at UMD in this direction. We present our results on several experiments suggested in FRGC. We believe that though pattern classification techniques play an extremely significant role in automatic face recognition under controlled conditions, physical modeling is required to generalize across varying situations. Accordingly, we describe a generative approach to recognize faces across varying illumination. Unlike most current methods, our method does not ignore shadows. Instead we use them to our benefit by modeling attached shadows in our formulation.