用于虹膜识别的透明隐形眼镜检测：一种两阶段掩码引导注意力方法

中文摘要 AI 辅助

本研究聚焦虹膜识别场景中透明隐形眼镜的影响与检测问题。尽管在呈现攻击检测（PAD）范式下，对装饰性或带图案隐形眼镜的检测已得到广泛研究，但透明的处方隐形眼镜因应用广泛却受到相对较少关注。与带图案隐形眼镜不同，透明隐形眼镜不会引入显著的纹理伪影，难以检测，且常被认为对虹膜识别无影响。我们首先使用商用VeriEye匹配器在四个基准数据集上验证这一假设，结果显示透明隐形眼镜会轻微降低 genuine 匹配分数并增加验证误差。随后我们提出一种两阶段隐形眼镜检测框架：第一阶段使用现有PAD模型识别带图案隐形眼镜；第二阶段采用配备掩码引导空间注意力（MGSA）的ConvNeXt-Base模型，聚焦更具挑战性的透明隐形眼镜与无隐形眼镜的区分。所提MGSA模块结合霍夫变换衍生的解剖感兴趣区域（ROI）掩码、学习到的空间注意力及挤压-激励通道重校准，使网络能聚焦与透明隐形眼镜佩戴相关的细微角膜缘线索。在四个数据集上，由带图案和透明隐形眼镜检测组成的完整管道准确率达90.0%至98.8%。最后，我们引入z分数校准方法，当输入图像中检测到透明隐形眼镜时调整VeriEye匹配分数，该校准使各数据集的等错误率（EER）降低4.1%至28.3%，证明可靠的透明隐形眼镜检测可直接提升虹膜验证性能。

英文摘要

This work focuses on the impact and detection of clear contact lenses in the context of iris recognition. While the detection of cosmetic or patterned contact lenses has been extensively studied under the presentation attack detection (PAD) paradigm, clear prescription contact lenses, that are typically transparent, have received comparatively less attention despite their widespread use. Unlike patterned lenses, clear lenses introduce no salient texture artifact, making them difficult to detect and are often assumed to have no impact on iris recognition. We first examine this assumption using the commercial VeriEye matcher on four benchmark datasets and show that clear lenses marginally degrade genuine match scores and increase verification error. We then propose a two-stage contact-lens detection framework. Stage~1 uses an existing PAD model to identify patterned lenses, while Stage~2 focuses on the more challenging clear-lens versus no-lens distinction using a ConvNeXt-Base model equipped with Mask-Guided Spatial Attention (MGSA). The proposed MGSA module incorporates a Hough-derived anatomical ROI mask together with learned spatial attention and Squeeze-and-Excitation channel recalibration, allowing the network to focus on subtle limbal cues associated with clear lens wear. Across four datasets, the full pipeline consisting of both patterned and clear contact lens detection achieves between 90.0\%--98.8\% accuracy. Finally, we introduce a z-score calibration method that adjusts VeriEye match scores when a clear lens is detected in the input images. This calibration reduces EER by 4.1\%--28.3\% across datasets, demonstrating that reliable clear contact lens detection can directly improve iris verification performance.

发表机构

• Michigan State University（密歇根州立大学）

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