Security Enhanced Multi-Factor Biometric Authentication System Using FFF and KSVM

Abstract

In this study we focus on multimodal biometric system by combining finger knuckle and finger vein using feature level fusion optimization. Biometric characteristics (Eyes, Finger vein, Finger Knuckle, Face, Ear, and Palm) like. Here used unique and secure password (like Finger Vein, Finger Knuckle). In this paper, the authors propose a multimodal biometric system by combining the finger knuckle and finger vein images at feature-level fusion using fractional firefly (FFF) optimization. Biometric characteristics, like finger knuckle and finger vein are unique and secure. Initially, the features are extracted from the finger knuckle and finger vein images using repeated line tracking method. Then, a newly developed method of feature-level fusion using FFF optimization is used. This method is utilized to find out the optimal weight score to fuse the extracted feature sets of finger knuckle and finger vein images. Thus, the recognition is carried out by the fused feature set using layered k-SVM (k-support vector machine) which is newly developed by combining the layered SVM classifier and k-neural network classifier. The experimental results are evaluated and the performance is analyzed with false acceptance ratio, false rejection ratio and accuracy. The outcome of the proposed FFF optimization system obtains a higher accuracy.