Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning
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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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