Detecting Clear Contact Lenses for Iris Recognition: A Two-Stage Mask-Guided Attention Approach
2026-08-10 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionArtificial Intelligence
AI summaryⓘ
The authors studied how clear contact lenses, which are hard to spot and often ignored, affect iris recognition systems. They found that clear lenses slightly reduce the accuracy of recognizing people by their iris patterns. To fix this, they created a two-step method: one for detecting patterned lenses and another using a special attention-based model to spot clear lenses by focusing on subtle eye features. Their approach worked well across several datasets, and by adjusting recognition scores when clear lenses were detected, they improved the overall system performance.
iris recognitioncontact lensespresentation attack detection (PAD)convnextspatial attentionverification error rate (EER)Hough transformregion of interest (ROI)mask-guided attentionscore calibration
Authors
Parisa Farmanifard, Arun Ross
Abstract
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.