Technology advancements have led to the emergence of biometrics as the most relevant future authentication technology. On practical grounds, unimodal biometric authentication systems have inevitable momentous limitations due to varied data quality and noise levels. The paper aims at investigating fusion of face and fingerprint biometric characteristics to achieve a high level personal authentication system. In the fusion strategy face features are extracted using Scale-Invariant Feature Transform (SIFT) algorithm and fingerprint features are extracted using minutiae feature extraction. These extracted features are optimized using nature inspired Genetic Algorithm (GA). The efficiency of the proposed fusion authentication system is enhanced by training and testing the data by applying Artificial Neural Network (ANN). The quality of the proposed design is evaluated against two nature inspired algorithms, namely, Particle Swarm Optimization (PSO)and Artificial Bee Colony (ABC) in terms of False Acceptance Rate (FAR), False Rejection Rate (FRR) and recognition accuracy. Simulation results over a range of image sample from 10 to 100 images have shown that the proposed biometric fusion strategy resulted in FARof 2.89, FAR 0.71and accuracy 97.72%.Experimental evaluation of the proposed system also outperformed the existing biometric fusion system.
Novel Biometric Fusion System using GA-PSO and ANN
