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.

主要关注的问题之一是社会中个人隐私的安全和保护。近年来，生物识别方法得到了发展，并广泛用于许多地方和设备中，以保护信息和资产。手腕静脉在体内，每个人的静脉纹路都是独一无二的。本文使用 PUT 手腕静脉数据集，包括手掌和手腕静脉图像，每个部分有 1200 幅左右手图像。通过应用分数傅里叶变换（FrFT）在时频域分析手腕静脉图像，提取的特征包括FrFT系数的相位、幅度、实部和虚部。由于实现 FrFT 的特征数量非常多，因此将接收器操作特性 (ROC) 应用于特征评分，并通过该工具选择最佳特征。支持向量机 (SVM) 用于对真实样本和冒名顶替样本进行分类。比较了 FrFT 提取的各种特征的结果，根据得到的结果，我们推断相位特征比其他特征强于基于手腕静脉图像的人员认证，并且该特征达到了 100% 的准确率。