Iris-SAM: Iris Segmentation Using a Foundational Model
Parisa Farmanifard Arun Ross
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%.