Biometric Security with a Robust Multimodal Features Level Fusion Using Modify Incremental Principal Component Analysis

Abstract

Biometric Authentication (BA) has been recently identified as a significant paradigm on maintaining the security level to improve individual person’s authenticity. With more and more advanced technology, the BA has to be enhanced to cope up with the increased insecure environment. Several schemes employing multimodal biometric system fuses the face image with certain other traditional biometric modalities to ensure security. However, changes in the facial expression significantly updates the facial geometry, thereby reduces the ranking score level. Some of the multimodal biometric research group has designed different algorithms on personal biometric feature based authentication. But, fusion multimodal biometric system did not provide robustness compromising the security level. To develop high robust multimodal biometric system, person multimodal biometric using Covariance Matrix Incremental Principal Component Analysis (CMI-PCA) method is proposed in this paper. The main goal of CMI-PCA method is to work with three principle steps and attain rank level fusion integration on the faces, ears and hand dorsal vein. First, the person’s multimodal biometric features are sensed and feature extraction is performed using Gabor Filter based Incremental PCA to improve robustness level. Second step is to match the extracted features with stored test image from the database using Score Matching based on Covariance Matrix Incremental PCA. The score matching based on Covariance Matrix Incremental PCA maintain the scale of extracted features and compute the mean score value to match with test images without any Covariance Matrix range. Finally, the CMI-PCA method involves combining different biometric identification ranks for making final decision. The extended Borda Count Multimodal Ranking system is used in CMI-PCA to determine the integrated biometric outcome and ensures higher security level for individual information. Experiment is conducted on factors such as robustness level, multimodal matching score rate, rank level fusion efficiency rate.