  This paper presents a novel automatic face recognition approach based on
local binary patterns. This descriptor considers a local neighbourhood of a
pixel to compute the feature vector values. This method is not very robust to
handle image noise, variances and different illumination conditions. We address
these issues by proposing a novel descriptor which considers more pixels and
different neighbourhoods to compute the feature vector values. The proposed
method is evaluated on two benchmark corpora, namely UFI and FERET face
datasets. We experimentally show that our approach outperforms state-of-the-art
methods and is efficient particularly in the real conditions where the above
mentioned issues are obvious. We further show that the proposed method handles
well one training sample issue and is also robust to the image resolution.
