FRMSDNET Classifier for Multimodal Feature Fusion Biometric Authentication
Keywords:
Adaptive Ostu Mode Segmentation Technique (AOMS), Kernel Snake Control Method (KSCM), Levy Good, The Bad, and the Ugly Optimizer (LGBUO), Fuzzy Residual Mean Squared Deviation Network (FRMSDNET) Classifier, deep learning.Abstract
Biometric System (BS), which requires biometric information from people grounded on their physical characteristics and/or behavioral attributes like Fingerprint (FP), iris, face, or voice pattern, is a pattern recognition system. Unimodal BS has a number of issues in real-world applications. It may include the person’s age factor, degradation of biological traits, and depending on just one trait that loses the authentication credits. A novel multi-BS grounded on the FP and iris has been proposed to get over the issue with unimodal systems. Two biometric characteristics, namely FP and iris are utilized in the proposed work. Initially, the FP image is pre-processed; in addition, by employing the Adaptive Ostu Mode segmentation (AOMS), the FP region is segmented. Next, the binarization and injection of segmented images into the ridge thinning procedure are done; next, the minutiae points are extracted. Afterward, the Iris image is pre-processed. Also, by deploying Kernel Snake Control Method (KSCM), the regions of the iris are segmented. The features are extracted from the segmented regions. By utilizing the Levy Good, the Bad, and the Ugly Optimizer (LGBUO), the significant features are selected as of the obtained features from the 2 phases. Lastly, the selected features are fused, which is then fed to the Fuzzy Residual Mean Squared Deviation Network (FRMSDNET) Classifier. The experiential outcomes exhibited that the proposed model attained an enhanced performance.
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