Extracting features from wrist vein images using fractional fourier transform for person verification.

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.