Enhanced Local Binary Patterns for Automatic Face Recognition

This paper presents a novel automatic face recognition approach based on local binary patterns (LBP). LBP descriptor considers a local neighbourhood of a pixel to compute the features. This method is not very robust to handle image noise, variances and different illumination conditions. In this paper, we address these issues and extend the original LBP operator by considering more pixels and different neighbourhoods to compute the feature vector. The proposed method is evaluated on two benchmark corpora, namely UFI and FERET face datasets. We experimentally show that our approach is very efficient because it significantly outperforms several other state-of-the-art methods and is efficient particularly in the real conditions where the above mentioned issues are obvious.