Face Recognition using Feature of Integral Gabor-Haar Transformation

Gabor filters are widely known as one of the best representation for face recognition. Since raw Gabor representation is of very high dimensionality, feature reduction is usually required in practice. This paper proposes the feature of integral Gabor-Haar transformation (FIGHT), which is a compact Gabor feature representation while still keeps high recognition performance. This paper also studies fusion strategies for groups of FIGHT feature, and present a discriminative learning scheme to combine group-wise results. Experiments show that FIGHT feature is effective, and the discriminative fusion over FIGHT feature group achieves the state-of-the-art performance on FERET database.