Iris finger vein finger print based multimodal biometric authentication algorithm using score level fusion with hybrid ga pso

Abstract

Biometric is emerging technology in identification and authentication of human being with more reliable and accurate. It is hard to imitate, forge, share, distribute and cannot be stolen, forgotten. After September 11, 2001 incident the biometric technologies are focused more. Combining multiple biometric systems is a promising solution to provide more security. It eliminates the disadvantages of unimodal biometric systems such as non-universality, noise in sensed data, intra-class variations, distinctiveness, spoof attacks and traditional method of authenticating a human and their identity. The proposed method depicts a multimodal biometric algorithm which is designed to recognize individuals for robust and secured authentication using normalized score level fusion techniques with hybrid Genetic Algorithm and Particle Swarm Optimization for optimization in order to reduce False Acceptance Rate and False Rejection Rate and to enhance accuracy. In this research work, the multimodal biometric algorithm integrates Iris, Finger Vein and Finger Print biometric traits for their best biometric characteristics. Each biometric trait is adapted for preprocessing techniques such as localization and normalization, before recognition in order to improve the image quality and recognition rate, each trait is recognized by individual recognition algorithm. newline