Evolutionary Optimization for Biometric Feature Identification and Recognition

Performance variability is a major difficulty for multimodal biometric systems, affecting their consistency and dependability in person identification. In order to solve this issue, the use of ensemble learning techniques is suggested in this work. The predictions of several classifiers are combined in ensemble learning to increase system consistency and performance as a whole. The unpredictability inherent in multimodal biometric systems is lessened by ensemble approaches, which integrate many classifiers trained on various subsets of data. By producing identification results that are more reliable and stable under many circumstances, this method improves system reliability. Improved accuracy and consistency can be achieved by integrating complementing data from many biometric modalities using ensemble learning techniques including bagging, boosting, and stacking. In practical applications, the suggested method shows promise in reducing performance variability and improving the dependability of multimodal biometric systems.