Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning

• URL: http://arxiv.org/abs/2110.10334v1
• Date: Wed, 20 Oct 2021 01:37:19 GMT
• Title: Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning
• Authors: Jianze Wei, Huaibo Huang, Muyi Sun, Ran He, Zhenan Sun
• Abstract summary: We propose an Iris U-transformer (IrisUsformer) for accurate and reliable iris segmentation. For better accuracy, we elaborately design IrisUsformer by adopting position-sensitive operation and re-packaging transformer block. We show that IrisUsformer achieves better segmentation accuracy using 35% MACs of the SOTA IrisParseNet.
• Score: 96.72850130126294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As an upstream task of iris recognition, iris segmentation plays a vital role in multiple subsequent tasks, including localization and matching. A slight bias in iris segmentation often results in obvious performance degradation of the iris recognition system. In the paper, we propose an Iris U-transformer (IrisUsformer) for accurate and reliable iris segmentation. For better accuracy, we elaborately design IrisUsformer by adopting position-sensitive operation and re-packaging transformer block to raise the spatial perception ability of the model. For better reliability, IrisUsformer utilizes an auxiliary head to distinguishes the high- and low-uncertainty regions of segmentation predictions and then adopts a weighting scheme to guide model optimization. Experimental results on three publicly available databases demonstrate that IrisUsformer achieves better segmentation accuracy using 35% MACs of the SOTA IrisParseNet. More importantly, our method estimates the uncertainty map corresponding to the segmentation prediction for subsequent processing in iris recognition systems.

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