A Novel Multimodal Biometric Authentication Framework Using Rule-Based ANFIS Based on Hybrid Level Fusion

Unimodal Bio-Metric (BM) systems are vulnerable to changes in an individual’s BM features in addition to presentation attacks; thus, in identifying individuals, they possess only limited effectiveness. For attaining higher dependability of BM authentication, a multi-modal BM has been implemented in authentication systems. By utilizing a Rule-based Adaptive Neuro-Fuzzy Inference System (R-ANFIS), an effectual Feature-Score-Rank (FSR) fusion-centered Multi-Modal Biometric Authentication (MMBA) has been proposed here. Face, eye, and Fingerprint (FP) are the ‘3’ images, which are taken as of the person's SDUMLA-HMT database, regarded as input in MMBA. In the proposed methodology, (i) Face image segmentation utilizing Improved Viola-Jones Algorithm, (ii) Feature Extraction (FE) to form the segmented facial parts, and (iii) Feature Selection (FS) utilizing Chaos-based Salp Swarm Algorithm (CSSA) are the operations performed on the inputted face image after gathering the input data. After that, by means of the local miniature FE along with CSSA-FS, the FP image is processed. Next, by utilizing Canny Edge-centric Modified Circular Hough Transform and CSSA-FS, the eye image is processed via iris part segmentation. Subsequently, the chosen features of ‘3’ inputted images are fused in the sequence of the FSR level. Lastly, for identifying if the person is an authentic one or not, these fused features are inputted into the R-ANFIS. Then, experimentations are conducted to evaluate the proposed methodology’s performance. The experiential outcomes displayed that when analogized with the prevailing algorithms, the proposed model achieves superior performance.