{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:K6TCCTELKFZD6XRVU5PVTJT3IU","short_pith_number":"pith:K6TCCTEL","schema_version":"1.0","canonical_sha256":"57a6214c8b51723f5e35a75f59a67b4505079398e233164ba9c90e76ebddaf45","source":{"kind":"arxiv","id":"2506.12447","version":3},"attestation_state":"computed","paper":{"title":"CLIP-HandID: Vision-Language Model for Hand-Based Person Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amudhavel Jayavel, Babu Pallam, Nathanael L. Baisa","submitted_at":"2025-06-14T10:59:00Z","abstract_excerpt":"This paper introduces a novel approach to person identification using hand images, designed specifically for criminal investigations. The method is particularly valuable in serious crimes such as sexual abuse, where hand images are often the only identifiable evidence available. Our proposed method, CLIP-HandID, leverages a pre-trained foundational vision-language model - CLIP - to efficiently learn discriminative deep feature representations from hand images (input to CLIP's image encoder) using textual prompts as semantic guidance. Since hand images are labeled with indexes rather than text "},"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":"2506.12447","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-06-14T10:59:00Z","cross_cats_sorted":[],"title_canon_sha256":"5943571554878beaeb1a877529bcb51d30f6f457f92a97a924069fad7c6c872c","abstract_canon_sha256":"abf6b1d324830a726a7a58e4ab30b86bacae17769ab38b67feb83ce438072540"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:45:36.384225Z","signature_b64":"LJ+wOYvg6ebZvA93BXSEKpUwY+nfdcPZAYd1gP6kzUhiz1P/yMDCWhP8QJRy2BbZMX8bPcqkEG3LJ1Zz/fKRDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57a6214c8b51723f5e35a75f59a67b4505079398e233164ba9c90e76ebddaf45","last_reissued_at":"2026-07-05T11:45:36.383575Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:45:36.383575Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CLIP-HandID: Vision-Language Model for Hand-Based Person Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Amudhavel Jayavel, Babu Pallam, Nathanael L. Baisa","submitted_at":"2025-06-14T10:59:00Z","abstract_excerpt":"This paper introduces a novel approach to person identification using hand images, designed specifically for criminal investigations. The method is particularly valuable in serious crimes such as sexual abuse, where hand images are often the only identifiable evidence available. Our proposed method, CLIP-HandID, leverages a pre-trained foundational vision-language model - CLIP - to efficiently learn discriminative deep feature representations from hand images (input to CLIP's image encoder) using textual prompts as semantic guidance. Since hand images are labeled with indexes rather than text "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12447","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/2506.12447/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":"2506.12447","created_at":"2026-07-05T11:45:36.383657+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.12447v3","created_at":"2026-07-05T11:45:36.383657+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12447","created_at":"2026-07-05T11:45:36.383657+00:00"},{"alias_kind":"pith_short_12","alias_value":"K6TCCTELKFZD","created_at":"2026-07-05T11:45:36.383657+00:00"},{"alias_kind":"pith_short_16","alias_value":"K6TCCTELKFZD6XRV","created_at":"2026-07-05T11:45:36.383657+00:00"},{"alias_kind":"pith_short_8","alias_value":"K6TCCTEL","created_at":"2026-07-05T11:45:36.383657+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.12134","citing_title":"MULTI: Disentangling Camera Lens, Sensor, View, and Domain for Novel Image Generation","ref_index":3,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K6TCCTELKFZD6XRVU5PVTJT3IU","json":"https://pith.science/pith/K6TCCTELKFZD6XRVU5PVTJT3IU.json","graph_json":"https://pith.science/api/pith-number/K6TCCTELKFZD6XRVU5PVTJT3IU/graph.json","events_json":"https://pith.science/api/pith-number/K6TCCTELKFZD6XRVU5PVTJT3IU/events.json","paper":"https://pith.science/paper/K6TCCTEL"},"agent_actions":{"view_html":"https://pith.science/pith/K6TCCTELKFZD6XRVU5PVTJT3IU","download_json":"https://pith.science/pith/K6TCCTELKFZD6XRVU5PVTJT3IU.json","view_paper":"https://pith.science/paper/K6TCCTEL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.12447&json=true","fetch_graph":"https://pith.science/api/pith-number/K6TCCTELKFZD6XRVU5PVTJT3IU/graph.json","fetch_events":"https://pith.science/api/pith-number/K6TCCTELKFZD6XRVU5PVTJT3IU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K6TCCTELKFZD6XRVU5PVTJT3IU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K6TCCTELKFZD6XRVU5PVTJT3IU/action/storage_attestation","attest_author":"https://pith.science/pith/K6TCCTELKFZD6XRVU5PVTJT3IU/action/author_attestation","sign_citation":"https://pith.science/pith/K6TCCTELKFZD6XRVU5PVTJT3IU/action/citation_signature","submit_replication":"https://pith.science/pith/K6TCCTELKFZD6XRVU5PVTJT3IU/action/replication_record"}},"created_at":"2026-07-05T11:45:36.383657+00:00","updated_at":"2026-07-05T11:45:36.383657+00:00"}