Multi-modal biometric recognition system based on FLSL fusion method and MDLNN classifier

Abstract

A Multi-Modal Biometrics (MMB) system incorporates information as of more than ‘1’ biometric modality for enhancing each biometric system’s performance.  Numerous prevailing research methodologies focused on MMB recognition. However, the recognition system encompasses robustness, accuracy, along with recognition rate issues. This paper proposed the MMB recognition system centred on the FLSL fusion method and Modified Deep Learning Neural Network (MDLNN) classifier in order to enhance the performance. The face, ear, retina, fingerprint, and front hand image traits are considered by the proposed method. It comprised image enhancement, segmentation, Feature Extractions (FE), Feature Reduction, feature fusion, rule generation, and identification phases. The Improved Plateau Histogram Equalization (IPHE) algorithm enhances all the inputted traits. After that, Viola-Jones Algorithm (VJA) segmented the facial parts, and the Penalty and Pearson correlation-based Watershed Segmentation (PPWS) algorithm eliminates the unwanted information in the ear and finger traits and also segmented the blood vessel of the retina image. Region of Interests (ROI) calculation separates the palm region. Next, the features are extracted as of images, and then, the Kernelized Linear Discriminants Analysis (KLDA) algorithm reduces the features’ dimensionalities. Next, the Features Level and Scores Level (FLSL) fusion method fuse the features. Therefore, the features’ fused output is inputted to the MDLNN to classify the person as genuine or imposter. The investigational evaluation of the proposed MDLNN with the prevailing classifiers is analyzed. The proposed MDLNN centred MMB recognition system trounces the top-notch methods.