ABSTRACT

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.

摘要

本文提出了一种基于称为加权均值模式（WEM）的描述符的纹理分类和生物特征认证的anovel方法。已经开发了提出的描述符，用于从作者创建的大型手图像数据集中提取纹理特征。该方法使用了指关节指纹（FKP）的鲜明特征来进行手图像检索，可用于生物特征识别系统。所提出的方法还包括用于消除不太重要的图案的特征选择步骤和用于量化图像的相似性的加权距离量度。该方法将支持向量机（SVM）用于分类阶段。所提出的方法已在FKP图像数据集上进行了测试，以评估图像的检索性能，并在Brodatz，Vistex，和Stex数据集来评估纹理分类的性能。通过与其他方法的比较证明了该方法的较高性能。所提出的方法对于各种应用（包括身份识别和分类）显示出足够的精确度。