Intelligent Techniques for Matching Palm Vein Images

The palm vein is one of the most reliable physiological characteristics that can be used to distinguish between individuals. Palm vein technology works by identifying the vein patterns in an individual's palm. The key techniques of palm vein recognition can systematically described in five parts extracting region of interest (ROI), preprocessing to image, extracting palm vein pattern, extracting features and features matching. In this paper we propose an image analysis method to extract (ROI) from palm vein image. After extracting ROI we design a sequence of preprocessing steps to remove the translation and rotation of palm vein images using Homomorphic filter and Canny filter to detect the edges on images. Then we present a comparison between three algorithms of feature extraction principal component analysis (PCA) , Scale invariant feature transform (SIFT) and Local Binary Patterns (LBPs)algorithms with k-Nearest Neighbors (K-NN) classifier for matching using CASIA Multi-Spectral Palmprint Image Database V1.0 (CASIA database) .