A Cost-effective Palm Vein Recognition System

Abstract

Nowadays, traditional identity verification methods like typing in passwords have been gradually replaced by verification based on your physiological characteristics or behavioural patterns, known as biometrics because of their advantages in security and reliability. Palm vein recognition is outstanding compared to other techniques because of the difficulty in circumvention. However, the existing palm vein recognition devices on the market are extremely expensive. In this project, we developed the software based on MFC framework and MATLAB with a low-cost sensor module produced by a 3D printer. However, we still achieve a very high precision in recognition. Multi-scale and multi-direction Gaussian filter is first applied to extract the palm vein of ROI. Then the image is transformed into a binary image and small noisy regions are eliminated from the binary image. After that, registration is applied to align two images. Finally, SVM classifier estimates the similarity value and makes an identification decision. The evaluation of this system is based on a database of 1156 data sets enrolled by 34 subjects and the result shows the accurate classification performance of our system.