Biometrics is the study of unique characteristics present in the human body such as fingerprint, palm-print, retina, iris, footprint, etc. While other traits have been explored widely, only a few people have been considered the foot-palm region, despite having unique properties. Prior work has explored the foot shape features using length, width, major axis, minor axis, centroid, etc. but they are not reliable for personal verification due to similarity in the physical composition of two persons. It increases the demand for more unique features based on the footprint. Footprint texture features coming from creases of foot palm are unique and permanent like palmprint texture features. Hence the main objective of the paper is to investigate various kinds of texture feature techniques. These techniques will be further used in correct extraction of footprint features. After extraction of footprint features a detailed experimental analysis is performed to discover the uniqueness in foot texture. It is further utilized to test its viability as a human recognition trait. We describe a detailed feature extraction and classification technique applied to a collected footprint data-set. For feature extraction, we use three techniques: Gray Level Co-occurrence Matrix (GLCM), Histogram Oriented Gradient (HOG), and Local Binary Patterns (LBP). Feature classification is performed using four techniques: Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Ensemble Subspace Discriminant (ESD). GLCM provides less accuracy, while HOG generates a big feature vector which takes more execution time. LBP provides a trade-off between the accuracy and the execution time. Detailed quantitative experiments show: GLCM with LDA provides an accuracy of 88.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$88.5\%$$\end{document}, HOG with Fine-KNN achieves 86.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$86.5\%$$\end{document} accuracy and LBP with LDA achieves the accuracy of 97.9%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$97.9\%$$\end{document}.

中文翻译：

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基于纹理特征的基于足迹生物特征的人员验证方法

生物识别学是对存在于人体中的独特特征的研究，例如指纹、掌纹、视网膜、虹膜、足迹等。虽然其他特征已被广泛探索，但只有少数人被认为是足掌区域，尽管具有独特的属性。先前的工作已经使用长度、宽度、长轴、短轴、质心等探索了脚形特征，但由于两个人的身体构成相似，它们对于个人验证并不可靠。它增加了对基于足迹的更多独特功能的需求。来自脚掌折痕的足迹纹理特征与掌纹纹理特征一样独特且永久。因此，本文的主要目的是研究各种纹理特征技术。这些技术将进一步用于正确提取足迹特征。提取足迹特征后，进行详细的实验分析以发现足部纹理的独特性。它被进一步用于测试其作为人类识别特征的可行性。我们描述了应用于收集的足迹数据集的详细特征提取和分类技术。对于特征提取，我们使用三种技术：灰度共生矩阵 (GLCM)、直方图定向梯度 (HOG) 和局部二进制模式 (LBP)。特征分类使用四种技术进行：线性判别分析 (LDA)、支持向量机 (SVM)、K-最近邻 (KNN) 和集成子空间判别 (ESD)。GLCM 提供的准确度较低，而 HOG 会生成较大的特征向量，这需要更多的执行时间。LBP 提供了准确性和执行时间之间的权衡。详细的定量实验表明：GLCM with LDA 提供了 88.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{ mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$88.5\%$$\end{document}，HOG with Fine-KNN 达到 86.5%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$86.5\%$$\end{document} 准确率和 LBP with LDA 达到了 97 的准确率。