通过课程学习提升动态虹膜识别的鲁棒性，捕捉时空特征。
CLRecogEye : Curriculum Learning towards exploiting convolution features for Dynamic Iris Recognition
- 将虹膜图像分段输入3D-CNN，学习时空特征表示。
- 课程学习策略使模型在旋转、模糊等干扰下仍保持高辨识度。
- 适合需要高精度虹膜识别的安防与身份认证场景。
虹膜认证算法已取得优异识别性能，适用于边境管控、公民识别及刑事调查等实际应用。然而，其鲁棒性仍受旋转、缩放、镜面反射和散焦模糊等因素影响。此外，现有方法多采用直接点对点比较（如余弦或L2距离），未能有效利用虹膜图案的时空结构。为此，本文提出一种新型通用匹配流程，旨在学习丰富的时空特征表示。首先沿一维分割虹膜图像，生成子图像序列作为3D-CNN输入，以捕捉空间与时空线索。为进一步增强时空特征建模能力，采用课程学习方式训练模型，使网络直接在特征空间中嵌入时间依赖关系，提升深度度量空间中的判别性。整个框架采用三元组损失与ArcFace损失端到端训练，具备强鲁棒性，可应对旋转、缩放、反射和模糊等挑战，提供一种稳健且通用的虹膜认证解决方案。
原文摘要 · Abstract (English)
Iris authentication algorithms have achieved impressive recognition performance, making them highly promising for real-world applications such as border control, citizen identification, and both criminal investigations and commercial systems. However, their robustness is still challenged by variations in rotation, scale, specular reflections, and defocus blur. In addition, most existing approaches rely on straightforward point-to-point comparisons, typically using cosine or L2 distance, without effectively leveraging the spatio-spatial-temporal structure of iris patterns. To address these limitations, we propose a novel and generalized matching pipeline that learns rich spatio-spatial-temporal representations of iris features. Our approach first splits each iris image along one dimension, generating a sequence of sub-images that serve as input to a 3D-CNN, enabling the network to capture both spatial and spatio-spatial-temporal cues. To further enhance the modeling of spatio-spatial-temporal feature dynamics, we train the model in curriculum manner. This design allows the network to embed temporal dependencies directly into the feature space, improving discriminability in the deep metric domain. The framework is trained end-to-end with triplet and ArcFace loss in a curriculum manner, enforcing highly discriminative embeddings despite challenges like rotation, scale, reflections, and blur. This design yields a robust and generalizable solution for iris authentication.Github code: https://github.com/GeetanjaliGTZ/CLRecogEye
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