Comparative Analysis of Feature Level Fusion Bimodal Biometrics for Access Control

Abstract

The increasing quest for dependable, robust and secure recognition systems led to combining two or more biometric modalities for improved performance of a biometric system. Bimodal biometric systems have proven to achieve obvious advantages over unimodal systems in various applications such as access control, surveillance, forensics, deduplication and border control etc. In this study, a comparative analysis of combination of three biometric trait at feature level of fusion was carried out. Face, fingerprint and iris images were acquired from LAUTECH biometric database. The bimodal setup consists of face-iris, face-fingerprint and iris-finger modalities. Principal Component Analysis (PCA) was employed for feature extraction, weighted sum technique was used to fuse the images at feature level while Support Vector Machine (SVM) was used for classification. Experimental result revealed that the bimodal biometric achieved an improved performance than the unimodal biometric. The performances of the bimodal systems indicated that combination of face and iris features achieved the best performance with FAR, FRR and accuracy of 0.00%, 1.42% and 99.00% at 38.15 seconds. Hence, a bimodal face-iris recognition system would produce a more reliable security surveillance system for access control than other combination compared in this study.