Robust two-stage face recognition approach using global and local features

This paper presents a robust two-stage face recognition approach that combines the traits of global features in first stage and the local features in second stage. The global features are extracted from Zernike moments (ZMs) method that encompasses the useful characteristics of being invariant to image rotation, scale, and noise. The local features are obtained from the histogram-based Weber Law Descriptor (WLD) having tremendous qualities like invariance to scale, change in image intensities, rotation, and noise. The novelty of this paper is twofold: (1) an efficient approach is used for combining the global and local features which is based on the human psychology to trace and memorize the known persons, i.e., locate some similar faces from the overall appearance of different persons and later identify from this the specific individual on the basis of their interior differences like shape of eyes, nose, etc.; (2) a method is used for providing the weights to individual face patches in extraction of local features, which is based on the averaged discrimination competence of features within a patch. The performance of proposed two-stage face recognition approach is analyzed against some major hurdles of this system, i.e., illumination, expression, scale, pose, occlusion, and noise variations. The proposed method achieves the highest recognition rate of 98.0% and 94.1% on ORL and Yale databases, respectively. The experimental results on these well-known face databases demonstrate that the proposed method is highly robust to illumination variation and also generates superior results against other variations.