Face recognition using scale-adaptive directional and textural features

A novel approach to face recognition problem using directional and texture information from face images, is proposed in this paper. In order to capture the directionality, specially designed using local polynomial approximation technique, scale adaptive digital filters are used. For texture features extraction, a low dimensional and computationally effective local descriptor is utilized. Textural and directional features are captured at the holistic and part based levels resulting in a robust face descriptor. The proposed method is tested on a number of standard test face datasets (ORL, XM2VTS, Extended Yale, CMU-PIE, AR, and FERET) for different scenarios and its performance is compared with several state-of-the-art techniques.