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

Abstract

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