New Weighted Mean-Based Patterns for Texture Analysis and Classification

This paper presents anovel method for texture classification and biometric authentication based on a descriptor called the weighted mean-based patterns (WEM). The proposed descriptor has been developed for extracting texture features from a large dataset of hand images, which has been created by the authors. The method uses the distinctive features of finger knuckle print (FKP) for hand image retrieval, which can be used in biometric identity recognition systems. The proposed method also includes a feature selection step for eliminating less important patterns and a weighted distance measure for quantifying the similarity of images. The method uses the support vector machine (SVM) for the classification stage. The proposed method has been tested on the FKP image dataset to evaluate the image retrieval performance, and also on Brodatz, Vistex, and Stex datasets to evaluate the performance for texture classification. Higher performance of the proposed method is demonstrated through comparison with other methods. The proposed method is shown to be sufficiently precise for a variety of applications, including identity recognition and classification.