The key factor in the performance of deep biometric models lies in their learning process. This study investigates different approaches to optimize the learning of deep neural networks, aiming to enhance their classification accuracy. We focus on methods that reduce overfitting and examine the impact of various hyperparameters. To conduct this research, we utilize models trained on the ImageNet dataset, which we fine-tune using transfer learning to classify people based on their ears. Furthermore, we assess the training times and average prediction speeds of individual models, considering hardware constraints. The results show that ResNet18 is the most suitable model for our training data, achieving best accuracy of 56%, closely followed by GoogLeNet with 51%
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