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

作者：Veluchamy S^*; Karlmarx L R

来源：IET Biometrics, 2017, 6(3): 232-242.

DOI：10.1049/iet-bmt.2016.0112

• image fusion
• feature extraction
• support vector machines
• image classification
• vein recognition
• optimisation
• edge detection
• neural nets
• multimodal biometric recognition
• finger knuckle image
• finger vein image
• feature-level fusion
• k-support vector machine classifier
• fractional firefly optimisation
• FFF optimisation
• biometric characteristics
• feature extraction
• repeated line tracking method
• optimal weight score
• layered k-SVM
• layered SVM classifier
• k-neural network classifier

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%.

• 出版日期2017-5