{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:5ZCB5NCSIHNMOFQZZSQLLN6CNF","short_pith_number":"pith:5ZCB5NCS","schema_version":"1.0","canonical_sha256":"ee441eb45241dac71619cca0b5b7c26967a47868a6c75e986f03de6f23f8ff27","source":{"kind":"arxiv","id":"1810.07599","version":1},"attestation_state":"computed","paper":{"title":"Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dihong Gong, Hao Wang, Tong Zhang, Wei Liu, Xing Ji, Yitong Wang, Zheng Zhou, ZhiFeng Li","submitted_at":"2018-10-17T15:08:11Z","abstract_excerpt":"As facial appearance is subject to significant intra-class variations caused by the aging process over time, age-invariant face recognition (AIFR) remains a major challenge in face recognition community. To reduce the intra-class discrepancy caused by the aging, in this paper we propose a novel approach (namely, Orthogonal Embedding CNNs, or OE-CNNs) to learn the age-invariant deep face features. Specifically, we decompose deep face features into two orthogonal components to represent age-related and identity-related features. As a result, identity-related features that are robust to aging are"},"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":"1810.07599","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-10-17T15:08:11Z","cross_cats_sorted":[],"title_canon_sha256":"0be75f4abeedee4677d93e7993c21c5e2219230eadf188163a933ef7a7bd31bc","abstract_canon_sha256":"8ad100b606f8536867a7684b61caf5991cf68bccb48d3b439cbfd7aa42374351"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:55.692759Z","signature_b64":"KgCb1CR+CGVQ9EWHjgK2WlgKuZvX5RGFWvVdpHxWZ/UWUEvIcNTi8JQgdvmgmlR7JrnrH1GJuD6B8+o+XNejCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ee441eb45241dac71619cca0b5b7c26967a47868a6c75e986f03de6f23f8ff27","last_reissued_at":"2026-05-18T00:02:55.692069Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:55.692069Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dihong Gong, Hao Wang, Tong Zhang, Wei Liu, Xing Ji, Yitong Wang, Zheng Zhou, ZhiFeng Li","submitted_at":"2018-10-17T15:08:11Z","abstract_excerpt":"As facial appearance is subject to significant intra-class variations caused by the aging process over time, age-invariant face recognition (AIFR) remains a major challenge in face recognition community. To reduce the intra-class discrepancy caused by the aging, in this paper we propose a novel approach (namely, Orthogonal Embedding CNNs, or OE-CNNs) to learn the age-invariant deep face features. Specifically, we decompose deep face features into two orthogonal components to represent age-related and identity-related features. As a result, identity-related features that are robust to aging are"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.07599","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":"1810.07599","created_at":"2026-05-18T00:02:55.692170+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.07599v1","created_at":"2026-05-18T00:02:55.692170+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.07599","created_at":"2026-05-18T00:02:55.692170+00:00"},{"alias_kind":"pith_short_12","alias_value":"5ZCB5NCSIHNM","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_16","alias_value":"5ZCB5NCSIHNMOFQZ","created_at":"2026-05-18T12:32:08.215937+00:00"},{"alias_kind":"pith_short_8","alias_value":"5ZCB5NCS","created_at":"2026-05-18T12:32:08.215937+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/5ZCB5NCSIHNMOFQZZSQLLN6CNF","json":"https://pith.science/pith/5ZCB5NCSIHNMOFQZZSQLLN6CNF.json","graph_json":"https://pith.science/api/pith-number/5ZCB5NCSIHNMOFQZZSQLLN6CNF/graph.json","events_json":"https://pith.science/api/pith-number/5ZCB5NCSIHNMOFQZZSQLLN6CNF/events.json","paper":"https://pith.science/paper/5ZCB5NCS"},"agent_actions":{"view_html":"https://pith.science/pith/5ZCB5NCSIHNMOFQZZSQLLN6CNF","download_json":"https://pith.science/pith/5ZCB5NCSIHNMOFQZZSQLLN6CNF.json","view_paper":"https://pith.science/paper/5ZCB5NCS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.07599&json=true","fetch_graph":"https://pith.science/api/pith-number/5ZCB5NCSIHNMOFQZZSQLLN6CNF/graph.json","fetch_events":"https://pith.science/api/pith-number/5ZCB5NCSIHNMOFQZZSQLLN6CNF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5ZCB5NCSIHNMOFQZZSQLLN6CNF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5ZCB5NCSIHNMOFQZZSQLLN6CNF/action/storage_attestation","attest_author":"https://pith.science/pith/5ZCB5NCSIHNMOFQZZSQLLN6CNF/action/author_attestation","sign_citation":"https://pith.science/pith/5ZCB5NCSIHNMOFQZZSQLLN6CNF/action/citation_signature","submit_replication":"https://pith.science/pith/5ZCB5NCSIHNMOFQZZSQLLN6CNF/action/replication_record"}},"created_at":"2026-05-18T00:02:55.692170+00:00","updated_at":"2026-05-18T00:02:55.692170+00:00"}