Iris-SAM: Iris Segmentation Using a Foundation Model

• URL: http://arxiv.org/abs/2402.06497v3
• Date: Fri, 31 May 2024 01:58:44 GMT
• Title: Iris-SAM: Iris Segmentation Using a Foundation Model
• Authors: Parisa Farmanifard, Arun Ross,
• Abstract summary: We develop a pixel-level iris segmentation model from a foundational model, viz., Segment Anything Model (SAM) The primary contribution of this work lies in the integration of different loss functions during the fine-tuning of SAM on ocular images. 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.
• Score: 10.902536447343465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%.

Related papers

• Adapting Segment Anything Model for Unseen Object Instance Segmentation [70.60171342436092]
  Unseen Object Instance (UOIS) is crucial for autonomous robots operating in unstructured environments. We propose UOIS-SAM, a data-efficient solution for the UOIS task. UOIS-SAM integrates two key components: (i) a Heatmap-based Prompt Generator (HPG) to generate class-agnostic point prompts with precise foreground prediction, and (ii) a Hierarchical Discrimination Network (HDNet) that adapts SAM's mask decoder.
  arXiv  Detail & Related papers  (2024-09-23T19:05:50Z)
• Handling Geometric Domain Shifts in Semantic Segmentation of Surgical RGB and Hyperspectral Images [67.66644395272075]
  We present first analysis of state-of-the-art semantic segmentation models when faced with geometric out-of-distribution data. We propose an augmentation technique called "Organ Transplantation" to enhance generalizability. Our augmentation technique improves SOA model performance by up to 67 % for RGB data and 90 % for HSI data, achieving performance at the level of in-distribution performance on real OOD test data.
  arXiv  Detail & Related papers  (2024-08-27T19:13:15Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Semantic segmentation of surgical hyperspectral images under geometric domain shifts [69.91792194237212]
  We present the first analysis of state-of-the-art semantic segmentation networks in the presence of geometric out-of-distribution (OOD) data. We also address generalizability with a dedicated augmentation technique termed "Organ Transplantation" Our scheme improves on the SOA DSC by up to 67 % (RGB) and 90 % (HSI) and renders performance on par with in-distribution performance on real OOD test data.
  arXiv  Detail & Related papers  (2023-03-20T09:50:07Z)
• Segmentation-free Direct Iris Localization Networks [0.0]
  This paper proposes an efficient iris localization method without using iris segmentation and circle fitting. We propose an iris localization network (ILN) that can directly localize pupil and iris circles with eyelid points from a low-resolution iris image. We also introduce a pupil refinement network (PRN) to improve the accuracy of pupil localization.
  arXiv  Detail & Related papers  (2022-10-19T09:13:39Z)
• Iris Recognition Based on SIFT Features [63.07521951102555]
  We use the Scale Invariant Feature Transformation (SIFT) for recognition using iris images. We extract characteristic SIFT feature points in scale space and perform matching based on the texture information around the feature points using the SIFT operator. We also show the complement between the SIFT approach and a popular matching approach based on transformation to polar coordinates and Log-Gabor wavelets.
  arXiv  Detail & Related papers  (2021-10-30T04:55:33Z)
• Toward Accurate and Reliable Iris Segmentation Using Uncertainty Learning [96.72850130126294]
  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.
  arXiv  Detail & Related papers  (2021-10-20T01:37:19Z)
• An approach to human iris recognition using quantitative analysis of image features and machine learning [0.5243460995467893]
  In this paper, an efficient framework for iris recognition is proposed in four steps. The results confirm that the proposed scheme can provide a reliable prediction with an accuracy of up to 99.64%.
  arXiv  Detail & Related papers  (2020-09-12T23:23:33Z)
• Segmentation-Aware and Adaptive Iris Recognition [24.125681602124477]
  The quality of iris images acquired at-a-distance or under less constrained imaging environments is known to degrade the iris matching accuracy. The periocular information is inherently embedded in such iris images and can be exploited to assist in the iris recognition under such non-ideal scenarios. This paper presents such a segmentation-assisted adaptive framework for more accurate less-constrained iris recognition.
  arXiv  Detail & Related papers  (2019-12-31T04:31:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.