Curvelet-based illumination invariant feature extraction for face recognition

This paper presents a curvelet-based illumination invariant feature extraction technique to solve the problem of varying illumination in face recognition. Multiband feature technique is employed to search the decomposed curvelet subbands for subbands which are insensitive to illumination variation. The two best performing subbands are then concatenated to form the Optimal Curvelet Subbands (OCS). To further improve the performance of OSC, histogram equalization is applied to enhance the contrast of the details. The proposed feature extraction method was evaluated on YaleB, EYaleB and AR database. The simulation results obtained shows that the proposed method outperforms its wavelet counterpart and that the extracted subbands are also applicable for other databases.