| 概要 |
The advancement of Artificial Intelligence (AI) significantly impacts biometric identification methods, with ear biometrics becoming a standout approach due to the unique and consistent features of th...e1) human ear. This study uses two datasets, UERC and IIT-D and develops three custom ear recognition models. The primary focus is on employing ensemble learning techniques to enhance the accuracy and robustness of ear recognition systems. Ensemble methods are particularly beneficial as they reduce errors and improve overall performance by combining multiple models. The individual custom models show promising results, with Model 1, Model 2, and Model 3 achieving accuracies of 97.1%, 96.9%, and 97.3% on the IIT-D dataset, and 84%, 91.1%, and 91.6% on the UERC dataset. Combining these models, the average ensemble achieves an accuracy of 97.8% on the IIT-D dataset and 95.2% on the UERC dataset. The weighted ensemble further improves the results, achieving an accuracy of 98.99% on the IIT-D dataset and demonstrating similarly enhanced performance on the UERC dataset with an accuracy of 97.8. These findings highlight the effectiveness of ensemble learning in significantly boosting the performance of ear recognition systems. Future research could delve into more advanced ensemble techniques, employ larger and more diverse datasets, and evaluate the practical applicability of ear biometrics in various conditions.続きを見る
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