<![CDATA[This study focuses on optimizing the performance of a three-modality biometric recognition system (fingerprints, facial and voice recognition) with global decision fusion, designed for access control to secure areas. When the biometric database contains a large volume of information, the verification module's processing time increases considerably due to the complexity of template comparisons. To address this issue, we implemented an optimization strategy based on parallel programming, specifically targeting the intensive processing loops within the verification module. Using Microsoft's Task Parallel Library, we parallelized all critical loops associated with the three biometric modalities. By effectively exploiting for and foreach statements, our parallelized implementation enables optimal distribution of tasks across available processor cores. We validated our approach by conducting repeated experiments on data sets of varying sizes (50 to 600 individuals), with a rigorous analysis of temporal performance. The results show a significant reduction in execution times: for 600 entries, the processing time goes from 1.68 ms in sequential mode to 0.77 ms in parallel mode. These performances were evaluated over several iterations to ensure the statistical reliability of the results, in particular by calculating averages and standard deviations and including error bars in the comparative graphs. The practical implications of this work are significant: the module can be deployed in corporate security systems, airports or banks, while respecting ethical considerations and privacy constraints. Finally, this work paves the way for future extensions, including the integration of other biometric modalities, deployment on distributed clusters or the adoption of more advanced parallelization frameworks.]]>
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