Research On Finger Vein Feature Extraction Algorithm

With the rapid development of information technology,today's society have become increasingly demanding on informatio n security,identification is an important means to ensure system security.Biometric technology is safer and more convenient than the traditional identification technology which is a use of human natural physiological characteristics or behavioral charact eristics to make identification method.Among the biometric technologies,fingerprint recognition technology is relatively mature,but with the promotion of the application,fingerprinting has shown a lot of shortcomings,such as a fingerprint is easily copied and forged.Finger vein recognition is a new non-contact biometric technology,which overcome the shortcomings of the acquisition object confined to the surface effectively,and it has broad application prospects.In practical applications,due to t he limitations of the acquisition of equipment,the acquisition of the finger vein images have poor quality so that the feature extraction is difficult,which will affect the accuracy of the identification system.To solve these problems,the paper conduct ed in-depth research on finger vein feature extraction algorithm,and proposed the vein feature extraction algorithms for high recognition rate.The main research is as follows:(1)We researched on the vein image processing based on wavelet transform.Firstly,we analyzed the application with wavelet transform image decomposition based on Mallat algorithm theory.Then considered three factors that wavelet basis functions selection,wavelet decomposition level determination and the determination of the wavelet decomposition sub-maps with vein image processing on different wavelet transform.Finally,experiments showed the suitable vein image wavelet function,decomposition level and exploded sub-maps.(2)This paper analyzed the choice of wavelet transform coeffic ients weights on the basis of wavelet transform,we present three improved algorithm,the one is feature fusion algorithm based on the weighted wavelet transform and component analysis,the second is decision fusion algorithm based on the weighted wavelet transform and component analysis,third is feature fusion algorithm based on the weighted wavelet transform and kernel component analysis.The results showed that improved algorithm greatly improving the system of recognition rate compared with traditional algorithm by analyzing the theoretical and experimental.