Finger Vein Feature Extraction Based On Immune Clone Algorithm

In modern society, the high-speed development of computer and network has shown unprecedented importance of information security. People want to have a more convenient and reliable way for identification, Biometric identification technology has brought it all possible. Finger vein, hidden within the body, has unique advantages in high speed, high precision, live identification and internal features. It's an effective solution to the traditional biometrics such as fingerprints which face many problems. With the vein recognition technology develops in the direction towards products, the demanding of extraction algorithms on the veins become increasing. Extraction algorithm based on the accuracy, also need to consider the balance of real-time detection.This study focused on the following aspects: Analyzed the current finger vein feature extraction algorithms,Through simulation and compares their advantages and disadvantages;According to features of the human finger vein images, the algorithm of image feature extraction based on the Ridgelet transformation is presented in this paper;In the consideration of the accuracy of the Extraction-Algorithm, the real time ability of the algorithm is also taken into account. According to the absorption feature of our finger towards the near-infrared, Based on the construction of background image, this paper proposed an approach to obtain the first residual error image, get the second residual error image through adjusting the scale factors, and finally indirectly gain the features of the vein;To solve the misjudgment of noise and vein information in features extraction from low quality images, a novel method based on LWF (Linear weighting function) immune-clone algorithm is proposed in this paper. The method can helps to boost the growth of the vein information and suppress the interference of noise.;Absorbed the idea of the immune clonal algorithm simplified the immune clone method and applied to the finger vein recognition on-chip system.