An Experimental Study of Feature Reduction Using PCA in Multi-Biometric Systems Based on Feature Level Fusion

A Multi-biometric System amalgamates the evidences collected from the multiple sources or single source for person recognition based on templates like fingerprint, palm print and iris. These evidences can be combined at various levels like pixel level, score level, feature level, and decision level. A rich set of information is present at feature level, but there is a problem of dimensionality which affects the recognition system. This study focuses on a dimensionality reduction technique Principal Component Analysis (PCA), which reduces the feature vector after feature level fusion in three different multi-biometric systems. The Pre and Post analysis of reduction compares the size of feature vector and recognition time per template in the following systems. Fingerprint based recognition where two evidences like bifurcations and ridge endings are fused, palmprint recognition where 2D-Gabor and Log-Gabor features are concatenated, and Iris Recognition in which Log-Gabor and 2D-Gabor features are integrated. And also Performance of the system is evaluated Based on False Acceptance Rate and False Rejection Rate. The study has been carried out on FVC 2004 fingerprint, IIT Delhi palmprint, and IIT Delhi Iris datasets. The results have shown that a drastic improvement in the multimodal system performance after reduction in all metrics.