Optimizer Sensitivity In Vision Transformerbased Iris Recognition: Adamw Vs Sgd Vs Rmsprop
This addresses the need for robust biometric authentication systems, but it is incremental as it focuses on evaluating existing optimizers rather than introducing new methods.
This paper tackled the problem of how optimizer choice affects the accuracy and stability of Vision Transformer-based iris recognition systems, finding that different optimizers lead to varying performance outcomes, though no concrete numbers are provided in the abstract.
The security of biometric authentication is increasingly critical as digital identity systems expand. Iris recognition offers high reliability due to its distinctive and stable texture patterns. Recent progress in deep learning, especially Vision Transformers ViT, has improved visual recognition performance. Yet, the effect of optimizer choice on ViT-based biometric systems remains understudied. This work evaluates how different optimizers influence the accuracy and stability of ViT for iris recognition, providing insights to enhance the robustness of biometric identification models.