{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:7HPX2H7PO6GVE3QVBQLV4T33EW","short_pith_number":"pith:7HPX2H7P","schema_version":"1.0","canonical_sha256":"f9df7d1fef778d526e150c175e4f7b25a809a8b0f8b19243095c78dfac294eef","source":{"kind":"arxiv","id":"1608.04200","version":2},"attestation_state":"computed","paper":{"title":"Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Luc Van Gool, Ruiping Wang, Shiguang Shan, Xilin Chen, Zhiwu Huang","submitted_at":"2016-08-15T07:54:55Z","abstract_excerpt":"Riemannian manifolds have been widely employed for video representations in visual classification tasks including video-based face recognition. The success mainly derives from learning a discriminant Riemannian metric which encodes the non-linear geometry of the underlying Riemannian manifolds. In this paper, we propose a novel metric learning framework to learn a distance metric across a Euclidean space and a Riemannian manifold to fuse the average appearance and pattern variation of faces within one video. The proposed metric learning framework can handle three typical tasks of video-based f"},"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":"1608.04200","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-08-15T07:54:55Z","cross_cats_sorted":[],"title_canon_sha256":"cef93948e6cafcba3c80d723c1b19aa37a92b02d774b2400beb1a4f5e2a08102","abstract_canon_sha256":"098ccf0d5fd9c57b771bf41bbdb8c0e75f571f3e5d36ec53ae7fdafd260b886c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:53:15.538361Z","signature_b64":"qBv+g0NOoDwvRvN55OE2oU2ygrntieQjSrodIhieMSYWoXzVo3eQABMDfHZ337PRoLCGKF8N5OzDFg0Q4qzvCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9df7d1fef778d526e150c175e4f7b25a809a8b0f8b19243095c78dfac294eef","last_reissued_at":"2026-05-18T00:53:15.537993Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:53:15.537993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Luc Van Gool, Ruiping Wang, Shiguang Shan, Xilin Chen, Zhiwu Huang","submitted_at":"2016-08-15T07:54:55Z","abstract_excerpt":"Riemannian manifolds have been widely employed for video representations in visual classification tasks including video-based face recognition. The success mainly derives from learning a discriminant Riemannian metric which encodes the non-linear geometry of the underlying Riemannian manifolds. In this paper, we propose a novel metric learning framework to learn a distance metric across a Euclidean space and a Riemannian manifold to fuse the average appearance and pattern variation of faces within one video. The proposed metric learning framework can handle three typical tasks of video-based f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.04200","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":""},"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":"1608.04200","created_at":"2026-05-18T00:53:15.538058+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.04200v2","created_at":"2026-05-18T00:53:15.538058+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.04200","created_at":"2026-05-18T00:53:15.538058+00:00"},{"alias_kind":"pith_short_12","alias_value":"7HPX2H7PO6GV","created_at":"2026-05-18T12:30:04.600751+00:00"},{"alias_kind":"pith_short_16","alias_value":"7HPX2H7PO6GVE3QV","created_at":"2026-05-18T12:30:04.600751+00:00"},{"alias_kind":"pith_short_8","alias_value":"7HPX2H7P","created_at":"2026-05-18T12:30:04.600751+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/7HPX2H7PO6GVE3QVBQLV4T33EW","json":"https://pith.science/pith/7HPX2H7PO6GVE3QVBQLV4T33EW.json","graph_json":"https://pith.science/api/pith-number/7HPX2H7PO6GVE3QVBQLV4T33EW/graph.json","events_json":"https://pith.science/api/pith-number/7HPX2H7PO6GVE3QVBQLV4T33EW/events.json","paper":"https://pith.science/paper/7HPX2H7P"},"agent_actions":{"view_html":"https://pith.science/pith/7HPX2H7PO6GVE3QVBQLV4T33EW","download_json":"https://pith.science/pith/7HPX2H7PO6GVE3QVBQLV4T33EW.json","view_paper":"https://pith.science/paper/7HPX2H7P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.04200&json=true","fetch_graph":"https://pith.science/api/pith-number/7HPX2H7PO6GVE3QVBQLV4T33EW/graph.json","fetch_events":"https://pith.science/api/pith-number/7HPX2H7PO6GVE3QVBQLV4T33EW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7HPX2H7PO6GVE3QVBQLV4T33EW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7HPX2H7PO6GVE3QVBQLV4T33EW/action/storage_attestation","attest_author":"https://pith.science/pith/7HPX2H7PO6GVE3QVBQLV4T33EW/action/author_attestation","sign_citation":"https://pith.science/pith/7HPX2H7PO6GVE3QVBQLV4T33EW/action/citation_signature","submit_replication":"https://pith.science/pith/7HPX2H7PO6GVE3QVBQLV4T33EW/action/replication_record"}},"created_at":"2026-05-18T00:53:15.538058+00:00","updated_at":"2026-05-18T00:53:15.538058+00:00"}