Iris segmentation is a critical component of an iris biometric system and it
involves extracting the annular iris region from an ocular image. In this work,
we develop a pixel-level iris segmentation model from a foundational model,
viz., Segment Anything Model (SAM), that has been successfully used for
segmenting arbitrary objects. The primary contribution of this work lies in the
integration of different loss functions during the fine-tuning of SAM on ocular
images. In particular, the importance of Focal Loss is borne out in the
fine-tuning process since it strategically addresses the class imbalance
problem (i.e., iris versus non-iris pixels). Experiments on ND-IRIS-0405,
CASIA-Iris-Interval-v3, and IIT-Delhi-Iris datasets convey the efficacy of the
trained model for the task of iris segmentation. For instance, on the
ND-IRIS-0405 dataset, an average segmentation accuracy of 99.58% was achieved,
compared to the best baseline performance of 89.75%.

通过在眼球图像上利用不同的损失函数进行细调，我们开发了一个基于像素级的虹膜分割模型，其基础模型为 Segment Anything Model (SAM)，该模型在分割任意对象方面已取得成功。在细调过程中，Focal Loss 的重要性得到了证明，因为它能够有效地解决分类不平衡问题（即虹膜与非虹膜像素之间的区别）。对 ND-IRIS-0405、CASIA-Iris-Interval-v3 和 IIT-Delhi-Iris 数据集的实验表明，训练得到的模型在虹膜分割任务上具有很高的效能。例如，在 ND-IRIS-0405 数据集上，平均分割准确率达到了 99.58%，相比于最佳基准性能 89.75% 有了很大的提升。