Face Recognition Based on Statistical Texture Features

Abstract

Facial recognition has attracted the attention of researchers and has been one of the most prominent topics in the fields of image processing and pattern recognition since 1990. This resulted in a very large number of recognition methods and techniques with the aim of increasing the accuracy and robustness of existing systems. Many techniques have been developed to address the challenges and reliable recognition systems have been reached but require considerable processing time, suffer from high memory consumption and are relatively complex. The focus of this paper is on extracting subset of descriptors (less correlated and less calculations) from the co-occurrence matrix with the goal of enhancing the performance of Haralick’s descriptors. Improvements are achieved by adding the image preprocessing and selecting the proper method according to the database problem and by extracting features from image local regions. Keywords—face recognition;texture ,Haralick features; GLRLM; illumination pre-processing; local approach; SVM.