A novel face feature descriptor using adaptively weighted extended LBP pyramid

This paper proposes a new face description with single sample by Adaptively Weighted Extended Local Binary Pattern Pyramid (AWELBPP). First, the proposed algorithm utilizes pyramid transform to represent sample image into multi-resolution images. Second, the multi-resolution images are divided into a set of horizontal sub-images. Then, Extended Local Binary Pattern (ELBP) is applied to the sub-images in order to calculate the Sub-ELBP pyramid and the local image entropy is employed to the sub-images for an adaptively weighting map (AWM) that can measure the importance of the information they contain. Finally, AWELBPP feature is extracted from the fusion of the Sub-ELBP pyramid and the AWM. Under different illumination, facial expression and partial occlusion conditions, simulated experiments and comparisons on many subsets of Yale face databases, Yale B face databases and ORL face databases show that the proposed algorithm is an outstanding method for single sample face recognition compared with local PCA, ELBP, CLBP, PLBP. (C) 2013 Elsevier GmbH. All rights reserved.