A novel face feature descriptor using adaptively weighted extended LBP pyramid

Abstract

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.