access icon free System for multimodal biometric recognition based on finger knuckle and finger vein using feature-level fusion and k-support vector machine classifier

© The Institution of Engineering and Technology
Received 10/08/2016, Accepted 06/12/2016, Revised 10/11/2016, Published 12/12/2016

In this study, the authors propose a multimodal biometric system by combining the finger knuckle and finger vein images at feature-level fusion using fractional firefly (FFF) optimisation. 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 optimisation is used. This method is utilised 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 analysed with false acceptance ratio, false rejection ratio and accuracy. The outcome of the proposed FFF optimisation system obtains a higher accuracy of 96%.

Inspec keywords: optimisation; edge detection; vein recognition; support vector machines; image classification; neural nets; feature extraction; image fusion

Other keywords: fractional firefly optimisation; feature extraction; finger vein image; false acceptance ratio; multimodal biometric recognition; false rejection ratio; layered SVM classifier; layered k-SVM; biometric characteristics; optimal weight score; feature-level fusion; finger knuckle image; repeated line tracking method; k-neural network classifier; FFF optimisation; k-support vector machine classifier

Subjects: Optimisation techniques; Neural computing techniques; Computer vision and image processing techniques; Image recognition; Knowledge engineering techniques; Optimisation techniques

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