Improving face recognition by combination of natural and Gabor faces

Different approaches have been proposed over the last few years for improving holistic methods for face recognition. Some of them include color processing, different face representations and image processing techniques to increase robustness against illumination changes. One of the most successful strategies has shown to be the use of Gabor representation of the images. There has been also some research about the combination of different recognition methods, both at the feature and score levels. In this paper, we propose an effective combination scheme that is able to improve a single holistic method by fusing the recognition scores obtained from both natural face images and their Gabor representations. We have evaluated this scheme using some of the best known holistic approaches in the context of the Face Recognition Grand Challenge (FRGC). Results show at least 10% improvements in all cases. Moreover, this scheme also works when the scores are obtained from two different methods whenever one of them uses natural images and the other their Gabor representation. These results suggest that some complementariness exists between both representations, which can be easily exploited by fusion at the score level.