{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:FPZRWOGADYAMKEBKSJOWYXYFGM","short_pith_number":"pith:FPZRWOGA","schema_version":"1.0","canonical_sha256":"2bf31b38c01e00c5102a925d6c5f053300c479fefec7a7e80ee8a1d5c285cc2e","source":{"kind":"arxiv","id":"2402.06497","version":3},"attestation_state":"computed","paper":{"title":"Iris-SAM: Iris Segmentation Using a Foundation Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arun Ross, Parisa Farmanifard","submitted_at":"2024-02-09T16:08:16Z","abstract_excerpt":"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 "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2402.06497","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-02-09T16:08:16Z","cross_cats_sorted":[],"title_canon_sha256":"dae8f72fb5ae04e1a5962d1c93c702959ef128178696db1e7e6550be501eb43c","abstract_canon_sha256":"ad9d38cd9c029815c4e186bfef1639fdfab3329e27145ea32c0f98eb6d4f5c87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:25:31.664842Z","signature_b64":"XtA6ztO0+poui+MdPVJqO4vZt7kKD4fwqsxi5kMTeC9iz47Wkr6+6faUAejO5L3Y4k0RfG5Uy6JOlJNDtXCtBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2bf31b38c01e00c5102a925d6c5f053300c479fefec7a7e80ee8a1d5c285cc2e","last_reissued_at":"2026-07-05T08:25:31.664394Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:25:31.664394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Iris-SAM: Iris Segmentation Using a Foundation Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arun Ross, Parisa Farmanifard","submitted_at":"2024-02-09T16:08:16Z","abstract_excerpt":"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 "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.06497","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2402.06497/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2402.06497","created_at":"2026-07-05T08:25:31.664451+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.06497v3","created_at":"2026-07-05T08:25:31.664451+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.06497","created_at":"2026-07-05T08:25:31.664451+00:00"},{"alias_kind":"pith_short_12","alias_value":"FPZRWOGADYAM","created_at":"2026-07-05T08:25:31.664451+00:00"},{"alias_kind":"pith_short_16","alias_value":"FPZRWOGADYAMKEBK","created_at":"2026-07-05T08:25:31.664451+00:00"},{"alias_kind":"pith_short_8","alias_value":"FPZRWOGA","created_at":"2026-07-05T08:25:31.664451+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.05263","citing_title":"Can Foundation Models Generalise the Presentation Attack Detection Capabilities on ID Cards?","ref_index":29,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FPZRWOGADYAMKEBKSJOWYXYFGM","json":"https://pith.science/pith/FPZRWOGADYAMKEBKSJOWYXYFGM.json","graph_json":"https://pith.science/api/pith-number/FPZRWOGADYAMKEBKSJOWYXYFGM/graph.json","events_json":"https://pith.science/api/pith-number/FPZRWOGADYAMKEBKSJOWYXYFGM/events.json","paper":"https://pith.science/paper/FPZRWOGA"},"agent_actions":{"view_html":"https://pith.science/pith/FPZRWOGADYAMKEBKSJOWYXYFGM","download_json":"https://pith.science/pith/FPZRWOGADYAMKEBKSJOWYXYFGM.json","view_paper":"https://pith.science/paper/FPZRWOGA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.06497&json=true","fetch_graph":"https://pith.science/api/pith-number/FPZRWOGADYAMKEBKSJOWYXYFGM/graph.json","fetch_events":"https://pith.science/api/pith-number/FPZRWOGADYAMKEBKSJOWYXYFGM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FPZRWOGADYAMKEBKSJOWYXYFGM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FPZRWOGADYAMKEBKSJOWYXYFGM/action/storage_attestation","attest_author":"https://pith.science/pith/FPZRWOGADYAMKEBKSJOWYXYFGM/action/author_attestation","sign_citation":"https://pith.science/pith/FPZRWOGADYAMKEBKSJOWYXYFGM/action/citation_signature","submit_replication":"https://pith.science/pith/FPZRWOGADYAMKEBKSJOWYXYFGM/action/replication_record"}},"created_at":"2026-07-05T08:25:31.664451+00:00","updated_at":"2026-07-05T08:25:31.664451+00:00"}