Local Binary Patterns Calculated Over Gaussian Derivative Images

Overview & Synopsis

In this paper we present a new static descriptor for facial image analysis. We combine Gaussian derivatives with Local Binary Patterns to provide a robust and powerful descriptor especially suited to extracting texture from facial images. Gaussian features in the form of image derivatives form the input to the Linear Binary Pattern(LBP) operator instead of the original image. The proposed descriptor is tested for face recognition and smile detection. For face recognition we use the CMU-PIE and the YaleB+extended YaleB database. Smile detection is performed on the benchmark GENKI 4k database. With minimal machine learning our descriptor outperforms the state of the art at smile detection and compares favourably with the state of the art at face recognition.