{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:E46SVLPSGTNBOL4MZWLXTH4G33","short_pith_number":"pith:E46SVLPS","schema_version":"1.0","canonical_sha256":"273d2aadf234da172f8ccd97799f86dedc5bd3ac892c90e00e2566e3b1cbe747","source":{"kind":"arxiv","id":"2112.00849","version":2},"attestation_state":"computed","paper":{"title":"Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Adam Czajka, Aidan Boyd, Andrey Kuehlkamp, Dennis Chute, Eric Benjamin, Kevin Bowyer, Patrick Flynn","submitted_at":"2021-12-01T21:59:16Z","abstract_excerpt":"Iris recognition of living individuals is a mature biometric modality that has been adopted globally from governmental ID programs, border crossing, voter registration and de-duplication, to unlocking mobile phones. On the other hand, the possibility of recognizing deceased subjects with their iris patterns has emerged recently. In this paper, we present an end-to-end deep learning-based method for postmortem iris segmentation and recognition with a special visualization technique intended to support forensic human examiners in their efforts. The proposed postmortem iris segmentation approach "},"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":"2112.00849","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-12-01T21:59:16Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"22a3b824e18079a261fd7617fafed3bdebea5fb9e5a37ff24ac77dbb716dd571","abstract_canon_sha256":"7594db7d76c48529a3a56e94ade3d5f081570fdb69b6381bed6e6a6ecafaec85"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:44:26.169213Z","signature_b64":"3+1/r/FLkpoJjwc5WwHarEo+dJcjrHjC7z5dl97PNgmcexLGQIZUu6uVkVk12Rp4ERloQmBVMJKeV9anH7pdAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"273d2aadf234da172f8ccd97799f86dedc5bd3ac892c90e00e2566e3b1cbe747","last_reissued_at":"2026-07-05T03:44:26.168773Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:44:26.168773Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interpretable Deep Learning-Based Forensic Iris Segmentation and Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Adam Czajka, Aidan Boyd, Andrey Kuehlkamp, Dennis Chute, Eric Benjamin, Kevin Bowyer, Patrick Flynn","submitted_at":"2021-12-01T21:59:16Z","abstract_excerpt":"Iris recognition of living individuals is a mature biometric modality that has been adopted globally from governmental ID programs, border crossing, voter registration and de-duplication, to unlocking mobile phones. On the other hand, the possibility of recognizing deceased subjects with their iris patterns has emerged recently. In this paper, we present an end-to-end deep learning-based method for postmortem iris segmentation and recognition with a special visualization technique intended to support forensic human examiners in their efforts. The proposed postmortem iris segmentation approach "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.00849","kind":"arxiv","version":2},"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/2112.00849/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":"2112.00849","created_at":"2026-07-05T03:44:26.168837+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.00849v2","created_at":"2026-07-05T03:44:26.168837+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.00849","created_at":"2026-07-05T03:44:26.168837+00:00"},{"alias_kind":"pith_short_12","alias_value":"E46SVLPSGTNB","created_at":"2026-07-05T03:44:26.168837+00:00"},{"alias_kind":"pith_short_16","alias_value":"E46SVLPSGTNBOL4M","created_at":"2026-07-05T03:44:26.168837+00:00"},{"alias_kind":"pith_short_8","alias_value":"E46SVLPS","created_at":"2026-07-05T03:44:26.168837+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.17636","citing_title":"Surfacing Semantic Orthogonality Across Model Safety Benchmarks: A Multi-Dimensional Analysis","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E46SVLPSGTNBOL4MZWLXTH4G33","json":"https://pith.science/pith/E46SVLPSGTNBOL4MZWLXTH4G33.json","graph_json":"https://pith.science/api/pith-number/E46SVLPSGTNBOL4MZWLXTH4G33/graph.json","events_json":"https://pith.science/api/pith-number/E46SVLPSGTNBOL4MZWLXTH4G33/events.json","paper":"https://pith.science/paper/E46SVLPS"},"agent_actions":{"view_html":"https://pith.science/pith/E46SVLPSGTNBOL4MZWLXTH4G33","download_json":"https://pith.science/pith/E46SVLPSGTNBOL4MZWLXTH4G33.json","view_paper":"https://pith.science/paper/E46SVLPS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.00849&json=true","fetch_graph":"https://pith.science/api/pith-number/E46SVLPSGTNBOL4MZWLXTH4G33/graph.json","fetch_events":"https://pith.science/api/pith-number/E46SVLPSGTNBOL4MZWLXTH4G33/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E46SVLPSGTNBOL4MZWLXTH4G33/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E46SVLPSGTNBOL4MZWLXTH4G33/action/storage_attestation","attest_author":"https://pith.science/pith/E46SVLPSGTNBOL4MZWLXTH4G33/action/author_attestation","sign_citation":"https://pith.science/pith/E46SVLPSGTNBOL4MZWLXTH4G33/action/citation_signature","submit_replication":"https://pith.science/pith/E46SVLPSGTNBOL4MZWLXTH4G33/action/replication_record"}},"created_at":"2026-07-05T03:44:26.168837+00:00","updated_at":"2026-07-05T03:44:26.168837+00:00"}