发表机构
Texas State University（德克萨斯州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对虹膜纹理被遮挡时识别性能下降的问题，提出含遮挡类型识别、基于扩散重建及深度学习识别三个模块的框架，通过确定遮挡类别、重建受损区域并提取特征，提升了在CASIA-Iris-Thousand数据集上的虹膜识别性能。
AI 中文摘要
虹膜识别是一种可靠的生物识别方法，利用虹膜独特且稳定的纹理识别个体。然而，当有判别力的虹膜纹理被眼睑、睫毛、镜面反射或其他采集伪像部分遮挡时，识别性能会下降。现有方法常直接对退化样本进行识别或仅依赖剩余可见区域，在大量纹理受损时可能不足。我们提出一个具有三个连续模块的遮挡感知虹膜识别框架：遮挡类型识别、基于扩散的重建和基于深度学习的识别。首先，基于残差二维卷积神经网络的网络确定虹膜图像是否未被遮挡或属于受控遮挡类别之一。其次，被遮挡图像、二进制掩码和预测的遮挡类型为去噪扩散概率模型提供条件以重建受损区域。最后，VGG19-HPMNet（一种具有水平金字塔映射的改进VGG19模型）提取有判别力的全局和局部虹膜特征进行识别。在受控合成遮挡协议下对CASIA-Iris-Thousand数据集的实验表明，该框架通过识别遮挡类型、重建掩码区域和重新评估恢复的虹膜样本提高了虹膜识别性能。
英文摘要
Iris recognition is a reliable biometric approach that identifies individuals using the distinctive and stable texture of the iris. However, recognition performance can degrade when discriminative iris texture is partially occluded by eyelids, eyelashes, specular reflections, or other acquisition artifacts. Existing approaches often perform recognition directly on degraded samples or rely only on the remaining visible iris region, which may be inadequate when substantial texture is corrupted. To address this limitation, we propose an occlusion-aware iris recognition framework with three sequential modules: occlusion-type identification, diffusion-based reconstruction, and deep-learning-based recognition. First, a residual 2D CNN-based network determines whether an iris image is non-occluded or belongs to one of the controlled occlusion categories. Second, the occluded image, binary mask, and predicted occlusion type condition a denoising diffusion probabilistic model to reconstruct the corrupted region. Finally, VGG19-HPMNet, a modified VGG19 model with horizontal pyramid mapping, extracts discriminative global and part-wise local iris features for recognition. Experiments on the CASIA-Iris-Thousand dataset under a controlled synthetic-occlusion protocol show that the proposed framework improves iris recognition performance by identifying the occlusion type, reconstructing masked regions, and re-evaluating the restored iris samples.
CommentsAccepted by IEEE International Joint Conference on Biometrics (IJCB) 2026
