Expression, pose, and illumination invariant face recognition using lower order pseudo Zernike moments

Face recognition is an extremely challenging task with the presence of expression, orientation, and lightning variation. This paper presents a novel expression and pose invariant feature descriptor by combining Daubechies discrete wavelets transform and lower order pseudo Zernike moments. A novel normalization method is also proposed to obtain illumination invariance. The proposed method can recognize face images regardless of facial orientation, expression, and illumination variation using small number of features. An extensive experimental investigation is conducted using a large variation of facial orientation, expression, and illumination to evaluate the performance of the proposed method. Experimental results confirm that the proposed approach obtains high recognition accuracy and computational efficiency under different pose, expression, and illumination conditions.