Enhancing Security through Autonomous Learning Multi-Model Classification of 3D Palmprints: The ALMMo-0 Framework
Abstract
As research progresses, biometric technologies have achieved notable effectiveness in personal identification. However, despite these advancements, there persists a demand for improved performance in security applications. ALMMo-0, the Autonomous Learning Multi-Model Classifier of zeroth order introduces a novel approach to address this challenge. It effectively addresses the challenges of Enhancing global efficiency in supervised 3D palmprint identification, autonomy, non-iterative procedures, and complete transparency. This study presents an innovative method Utilizing ALMMo-0 for enhancing 3D fingerprint-based authentication systems. The approach utilizes a feedforward methodology driven solely by data and non-iterative processes, leveraging three primary Techniques such as Local Phase Quantization (LPQ), GIST, and Binarized Statistical Image Information (BSIF) are employed to extract relevant details from three-dimensional palmprint images.. Following feature extraction, ALMMo-0 autonomously generates AnYa Fuzzy Rule Base (FRB) sub-classifiers for individual categories, establishes a multimodal framework, and extracts data clouds. For authentication, The system utilizes a 'winner-takes-all' strategy for classifying incoming data, generating a confidence score that reflects the mutual distribution objectively. Tests performed on a 3D Palmprint dataset illustrate the efficacy of ALMMo-0, showcasing its performance using metrics Examples include metrics such as receiver operating characteristic (ROC) curves, rank-1 accuracy, equal error rate (EER), and cumulative match curve (CMC).. The experimental evaluations demonstrate outstanding performance of the proposed method, achieving perfect rank-1 accuracy, minimal EER, and significant features such as interpretability and computational efficiency.
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