Abstract

This paper proposes a new image representation for texture categorization and facial analysis, relying on the use of higher-order lo- cal differential statistics as features. In contrast with models based on the global structure of textures and faces, it has been shown recently that small local pixel pattern distributions can be highly discrimina- tive. Motivated by such works, the proposed model employs higher-order statistics of local non-binarized pixel patterns for the image description. Hence, in addition to being remarkably simple, it requires neither any user specified quantization of the space (of pixel patterns) nor any heuris- tics for discarding low occupancy volumes of the space. This leads to a more expressive representation which, when combined with discrimina- tive SVM classifier, consistently achieves state-of-the-art performance on challenging texture and facial analysis datasets outperforming contem- porary methods (with similar powerful classifiers).