Face Analysis, Description and Recognition using Improved Local Binary Patterns in One Dimensional Space

Abstract

In this study of biometric system, Improved One Dimensional Local Binary Patterns (I1DLBP) are developed and tested for use in face analysis, description and recognition. The extraction of face’s features is inspired from the principal that the human visual system combines between local and global features to differentiate between peoples. It starts by decomposing the facial image into several blocks with different resolutions. Then, each block is projected in one dimensional space. Next, the developed descriptor is applied on each projected block. Finally, Principal Component Analysis (PCA) is needed to reduce the dimensionalities of the concatenated vectors from each block and to keep only the pertinent information. K-nearest neighbor (KNN) is used as a classifier. Experiments were carried out under varying conditions in occlusion, rotation and, facial expressions using ORL and AR databases. Results show that the developed feature extraction approach can effectively describe the micro characteristics of the human face and the superiority in comparison to well-known and classical feature extraction descriptors.