The week's pick
Reseach Article
Feature Selection for Finger Knuckle Print-based Multimodal Biometric System
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 38 - Number 10 |
| Year of Publication: 2012 |
| Authors: Madasu Hanmandlu, Jyotsana Grover |
10.5120/4725-6905
|
Madasu Hanmandlu, Jyotsana Grover . Feature Selection for Finger Knuckle Print-based Multimodal Biometric System. International Journal of Computer Applications. 38, 10 ( January 2012), 27-33. DOI=10.5120/4725-6905
Abstract
In this paper, feature level fusion of finger knuckle prints (FKP’s) is implemented. To overcome the curse of dimensionality, feature selection using the triangular norms is proposed. There has been no effort on feature selection using the t-norms in the literature. In this paper we address the problem of feature selection on the finger knuckle print using the t-norms. An unknown parameter in t-norms is learnt using Reinforced Hybrid evolutionary technique. Feature level fusion is performed by combining the significant features of all FKP’s. Results show an improvement in the accuracy when the features are selected by a divergence function derived from the new entropy function using t-norms on two pairs of training features taken at a time. Results of both identi?cation and veri?cation rates show a signi?cant improvement in the performance with feature level fusion.
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