对比AdamW、SGD、RMSProp在ViT iris识别中的表现，提升系统稳定性。
Optimizer Sensitivity In Vision Transformerbased Iris Recognition: Adamw Vs Sgd Vs Rmsprop
- 比较三种优化器对ViT模型在虹膜识别中的影响
- 发现SGD在精度和稳定性上优于其他优化器
- 为生物特征识别模型选型提供实证依据
随着数字身份系统扩展，生物特征认证的安全性日益关键。虹膜识别凭借其独特且稳定的纹理模式具有高可靠性。深度学习的进展，尤其是视觉变换器（Vision Transformer, ViT），显著提升了视觉识别性能。然而，优化器选择对基于ViT的生物特征系统的影响仍缺乏研究。本文评估了不同优化器如何影响ViT在虹膜识别中的准确性和稳定性，为增强生物特征识别模型的鲁棒性提供洞见。
原文摘要 · Abstract (English)
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.
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