One of the major concerns is the security and protection of individuals' privacy in society. Biometric methods have been developed in recent years and they are widely used in many places and devices to protect information and assets. Wrist veins are inside the body and their pattern is unique for each person. In this paper, the PUT wrist vein dataset is used that comprises of palm and wrist vein images and each section has 1200 images of right and left hand. Wrist vein images are analyzed in the time-frequency domain by applying Fractional Fourier transform (FrFT), and the extracted features include phase, magnitude, real, and imaginary parts of FrFT coefficients. Since the number of features is very large by implementing FrFT, receiver operating characteristic (ROC) is applied for feature scoring and the best features are selected by this tool. Support Vector Machine (SVM) is used to classify real and impostor samples. The results of various features extracted by FrFT are compared, and according to the obtained results, we deduced that the phase feature is stronger than other features for person authentication based on wrist vein images, and this feature achieved 100% accuracy.

中文翻译：

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使用分数傅里叶变换从腕部静脉图像中提取特征以进行人员验证。

主要关注之一是社会中个人隐私的安全和保护。近年来，生物特征识别方法得到了发展，并且它们被广泛用于许多地方和设备中以保护信息和资产。腕部静脉位于体内，每个人的纹路都是独特的。在本文中，使用的PUT手腕静脉数据集由手掌和手腕静脉图像组成，每个部分的左右手图像均为1200。通过应用分数阶傅里叶变换（FrFT）在时频域中分析腕静脉图像，提取的特征包括FrFT系数的相位，幅度，实部和虚部。由于实施FrFT的功能非常多，接收机工作特性（ROC）用于功能评分，并通过此工具选择最佳功能。支持向量机（SVM）用于对真实样本和冒名顶替者进行分类。比较了通过FrFT提取的各种特征的结果，并根据获得的结果推论出，基于腕静脉图像的人认证中，相位特征比其他特征要强，并且该特征达到了100％的准确性。