An Improved Biometric Fusion System Based on Fingerprint and Face using Optimized Artificial Neural Network
Tajinder Kumar1, Shashi Bhushan2, Surender Jangra3
1Tajinder Kumar, Department of Computer Science, JKG Panjab Technical University, Kapurthala Punjab, India.
2Dr. Shashi Bhushan, Department of Computer Science, Chandigarh Engineering Collage, Mohali, Punjab, India.
3Dr.Surendrer Jangra, Department of Computer Science GTBC, Bhawanigarh, Punjab, India.
Manuscript received on 27 August 2019. | Revised Manuscript received on 05 September 2019. | Manuscript published on 30 September 2019. | PP: 1568-1575 | Volume-8 Issue-11, September 2019. | Retrieval Number: K18520981119/2019©BEIESP | DOI: 10.35940/ijitee.K1852.0981119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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%.
Keywords: Biometric Fusion, Face recognition, Fingerprint recognition, Feature Extraction, Feature Optimization, Classifier.
Scope of the Article: Application Artificial Intelligence and machine learning in the Field of Network and Database