Security of Multimodal Biometric Fusion System

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.

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Biometric Fusion System Using Face and Voice Recognition

Advances in Intelligent Systems and Computing - Advanced Computing and Systems for Security ◽

10.1007/978-981-13-8969-6_5 ◽

2019 ◽

pp. 71-89 ◽

Cited By ~ 1

Author(s):

Aleksander Kuśmierczyk ◽

Martyna Sławińska ◽

Kornel Żaba ◽

Khalid Saeed

Keyword(s):

Voice Recognition ◽

Fusion System ◽

Biometric Fusion

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An Improved Biometric Fusion System Based on Fingerprint and Face using Optimized Artificial Neural Network

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.k1852.0981119 ◽

2019 ◽

Vol 8 (11) ◽

pp. 1568-1575 ◽

Cited By ~ 1

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Recognition System ◽

Conventional Technique ◽

Optimization Techniques ◽

Fingerprint Recognition ◽

Fusion System ◽

Bacterial Foraging Optimization ◽

Artificial Neural ◽

Biometric Fusion

This research presents an improved biometric fusion system (IBFS) that integrates fingerprint and face as a subsystem. Two authentication systems, namely, Improved Fingerprint Recognition System (IFPRS) and Improved Face Recognition System (IFRS), are introduced respectively. For both, Atmospheric Light Adjustment (ALA) algorithm is used as an image quality enhancement technique for the improvement in visualization of acquired fingerprint and face data. Genetic Algorithm (GA) is used as an optimization algorithm with minutiae feature for IFPRS and Speed Up Robust Feature (SURF) for IFRS. Artificial Neural Network (ANN) is used as a classifier for IBFS. For the demonstration of the results, quality based parameters are computed, and in the end, a comparison is drawn to depict the efficiency of the work.The optimization techniques such as Particle Swarm Optimization (PSO) and BFO (Bacterial Foraging Optimization) has been considered to determine the effectiveness of the proposed model.The experimental results consider different parameters such as False Acceptance Rate (FAR), False Rejection Ratio (FRR), Accuracy and Execution time which shows that performance of the proposed model better than the other optimization models. In addition, to enhance robustness of the proposed structure, the results further compared with conventional technique which shows that accuracy has been improved by 2%.

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