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

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%.

Medical Systems Data Security and Biometric Authentication in Public Cloud Servers

“…[29] proposes a biometric fusion system with face and fingerprint modalities using Arithmetic Light Adjustment to enhance the quality of images, Generic Algorithm to optimize the minutiae features, and Speed Up Robust Feature (SURF) to optimize facial features, with an Artificial Neural Network as a classifier. [30] combined face and fingerprint modalities using feature and decision-level fusion. They then extracted features using Scale Invariant Feature Transform (SIFT) and fed the combination of vectors through a K-Nearest Neighbor (K-NN), Support Vector Machine, Naïve Bayes (NB), and Radial based on Function classifiers.…”