Iris-SAM: Iris Segmentation Using a Foundation Model
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%.
Code (1)
Tasks
Iris SegmentationmodelSegmentationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
DeepIrisNet2: Learning Deep-IrisCodes from Scratch for Segmentation-Robust Visible Wavelength and Near Infrared Iris Recognition
We first, introduce a deep learning based framework named as DeepIrisNet2 for visible spectrum and NIR Iris representation. The framework can work without classical iris normalization step or very accurate iris segmentat…
Iris RecognitionIris SegmentationSegmentationIris Recognition with Image Segmentation Employing Retrained Off-the-Shelf Deep Neural Networks
This paper offers three new, open-source, deep learning-based iris segmentation methods, and the methodology how to use irregular segmentation masks in a conventional Gabor-wavelet-based iris recognition. To train and va…
Deep LearningImage SegmentationIris RecognitionIris Segmentation+2Segmentation-free Direct Iris Localization Networks
This paper proposes an efficient iris localization method without using iris segmentation and circle fitting. Conventional iris localization methods first extract iris regions by using semantic segmentation methods such …
CPUDecoderIris RecognitionIris Segmentation+2Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework
Iris segmentation and localization in non-cooperative environment is challenging due to illumination variations, long distances, moving subjects and limited user cooperation, etc. Traditional methods often suffer from po…
DecoderIris SegmentationMedical Image SegmentationMulti-Task Learning+1Iris R-CNN: Accurate Iris Segmentation in Non-cooperative Environment
Despite the significant advances in iris segmentation, accomplishing accurate iris segmentation in non-cooperative environment remains a grand challenge. In this paper, we present a deep learning framework, referred to a…
Iris SegmentationRegion ProposalSegmentation