{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:A4D3NB4C7EMRFFOXKOFHKB6CFR","short_pith_number":"pith:A4D3NB4C","schema_version":"1.0","canonical_sha256":"0707b68782f9191295d7538a7507c22c78abffc427d5cd2f3f4282c476ec0782","source":{"kind":"arxiv","id":"1308.0275","version":1},"attestation_state":"computed","paper":{"title":"Domain-invariant Face Recognition using Learned Low-rank Transformation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ching-Hui Chen, Guillermo Sapiro, Qiang Qiu","submitted_at":"2013-08-01T17:34:36Z","abstract_excerpt":"We present a low-rank transformation approach to compensate for face variations due to changes in visual domains, such as pose and illumination. The key idea is to learn discriminative linear transformations for face images using matrix rank as the optimization criteria. The learned linear transformations restore a shared low-rank structure for faces from the same subject, and, at the same time, force a high-rank structure for faces from different subjects. In this way, among the transformed faces, we reduce variations caused by domain changes within the classes, and increase separations betwe"},"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":"1308.0275","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-08-01T17:34:36Z","cross_cats_sorted":[],"title_canon_sha256":"0172f87320734bb17712301ef760503039906f603a86e9977deb418ba3c27e10","abstract_canon_sha256":"1c97ce54b7142ca95c05b3b0c562e51b0369e3bebeea9a6b90f1b4fbc1fbaea1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:16:59.379610Z","signature_b64":"m81W6PbuOiZXYgT5YEhsyUfAeQpHbj0+0o/qmf70TxcM43XLWvSrP3bmF5/mCBLuBXyvK3U7IefOdACc/McFAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0707b68782f9191295d7538a7507c22c78abffc427d5cd2f3f4282c476ec0782","last_reissued_at":"2026-05-18T03:16:59.378758Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:16:59.378758Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Domain-invariant Face Recognition using Learned Low-rank Transformation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ching-Hui Chen, Guillermo Sapiro, Qiang Qiu","submitted_at":"2013-08-01T17:34:36Z","abstract_excerpt":"We present a low-rank transformation approach to compensate for face variations due to changes in visual domains, such as pose and illumination. The key idea is to learn discriminative linear transformations for face images using matrix rank as the optimization criteria. The learned linear transformations restore a shared low-rank structure for faces from the same subject, and, at the same time, force a high-rank structure for faces from different subjects. In this way, among the transformed faces, we reduce variations caused by domain changes within the classes, and increase separations betwe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1308.0275","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1308.0275","created_at":"2026-05-18T03:16:59.378907+00:00"},{"alias_kind":"arxiv_version","alias_value":"1308.0275v1","created_at":"2026-05-18T03:16:59.378907+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1308.0275","created_at":"2026-05-18T03:16:59.378907+00:00"},{"alias_kind":"pith_short_12","alias_value":"A4D3NB4C7EMR","created_at":"2026-05-18T12:27:38.830355+00:00"},{"alias_kind":"pith_short_16","alias_value":"A4D3NB4C7EMRFFOX","created_at":"2026-05-18T12:27:38.830355+00:00"},{"alias_kind":"pith_short_8","alias_value":"A4D3NB4C","created_at":"2026-05-18T12:27:38.830355+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/A4D3NB4C7EMRFFOXKOFHKB6CFR","json":"https://pith.science/pith/A4D3NB4C7EMRFFOXKOFHKB6CFR.json","graph_json":"https://pith.science/api/pith-number/A4D3NB4C7EMRFFOXKOFHKB6CFR/graph.json","events_json":"https://pith.science/api/pith-number/A4D3NB4C7EMRFFOXKOFHKB6CFR/events.json","paper":"https://pith.science/paper/A4D3NB4C"},"agent_actions":{"view_html":"https://pith.science/pith/A4D3NB4C7EMRFFOXKOFHKB6CFR","download_json":"https://pith.science/pith/A4D3NB4C7EMRFFOXKOFHKB6CFR.json","view_paper":"https://pith.science/paper/A4D3NB4C","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1308.0275&json=true","fetch_graph":"https://pith.science/api/pith-number/A4D3NB4C7EMRFFOXKOFHKB6CFR/graph.json","fetch_events":"https://pith.science/api/pith-number/A4D3NB4C7EMRFFOXKOFHKB6CFR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A4D3NB4C7EMRFFOXKOFHKB6CFR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A4D3NB4C7EMRFFOXKOFHKB6CFR/action/storage_attestation","attest_author":"https://pith.science/pith/A4D3NB4C7EMRFFOXKOFHKB6CFR/action/author_attestation","sign_citation":"https://pith.science/pith/A4D3NB4C7EMRFFOXKOFHKB6CFR/action/citation_signature","submit_replication":"https://pith.science/pith/A4D3NB4C7EMRFFOXKOFHKB6CFR/action/replication_record"}},"created_at":"2026-05-18T03:16:59.378907+00:00","updated_at":"2026-05-18T03:16:59.378907+00:00"}