Patch based descriptors for iris recognition

S. Emerich, R. Malutan, E. Lupu, László Lefkovits
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引用次数: 6

Abstract

In recent years, local texture analysis methods have gained increasing attention in many areas of image processing and computer vision. The current paper deals with iris features extraction, based on dense descriptors. A dense descriptor captures the local details, pixel by pixel over the complete image. Three different techniques were employed: Local Binary Pattern, Local Phase Quantization and Differential Excitation in order to provide both spatial and frequency information. To evaluate the proposed system, experiments were performed on the UPOL database, by using a linear SVM classification scheme. The results show that the iris micro-texture patterns such as crypts, furrows or pigment spots can be well characterized by patched based descriptors.
基于补丁的虹膜识别描述符
近年来,局部纹理分析方法在图像处理和计算机视觉的许多领域受到越来越多的关注。本文主要研究基于密集描述符的虹膜特征提取。密集描述符捕获局部细节,在整个图像上逐像素。为了同时提供空间和频率信息,采用了三种不同的技术:局部二进制模式、局部相位量化和差分激励。为了评估所提出的系统,使用线性支持向量机分类方案在UPOL数据库上进行了实验。结果表明,基于补丁的描述符可以很好地描述虹膜的隐窝、沟纹或色素斑等微观纹理图案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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