Face recognition using voting technique for the Gabor and LDP features

Face recognition can be described by a sophisticated mathematical representation and matching procedures. In this paper, Local Derivative Pattern (LDP) descriptors along with the Gabor feature extraction technique were used to achieve highest percentage of recognition possible. A robust comparison method, the Chi Square Distance, was used as a matching algorithm. Four databases involving different image capturing conditions: positioning, illumination and expressions were used. The best results were obtained after applying a voting technique to the Gabor and the LDP features.