A weighted voting scheme for recognition of faces with illumination variation
A. Nabatchian, Esam Abdel‐Raheem, Majid A. Ahmadi · 2010
A new method for face recognition based on weighted votes on different sub-images of a picture is proposed. The proposed method is robust under illumination variations and achieves the illumination invariants based on the reflectance-illumination model. The proposed method does not require any prior information about the face shape or illumination and can be applied on each image separately. It does not need multiple images in training stage to get the illumination invariants and is computationally efficient. Support vector machines are used as classifier. Several experiments are performed on Yale B and CMU-PIE databases. The system achieved 99.82% recognition rate in the Yale B and 99.74% for the CMU-PIE database.