{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PSWZL4HPXNOQCKKD66CLUQXMQC","short_pith_number":"pith:PSWZL4HP","schema_version":"1.0","canonical_sha256":"7cad95f0efbb5d012943f784ba42ec808903f11b3e9840770bdaf701af79b55f","source":{"kind":"arxiv","id":"1811.03478","version":1},"attestation_state":"computed","paper":{"title":"Multi-view Laplacian Eigenmaps Based on Bag-of-Neighbors For RGBD Human Emotion Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin Wang, Bixuan Du, Hong Qiao, Keye Zhang, Mingming Zhang, Shenglan Liu, Shuai Guo, Wenbo Luo, Yang Wang","submitted_at":"2018-11-08T15:03:58Z","abstract_excerpt":"Human emotion recognition is an important direction in the field of biometric and information forensics. However, most existing human emotion research are based on the single RGB view. In this paper, we introduce a RGBD video-emotion dataset and a RGBD face-emotion dataset for research. To our best knowledge, this may be the first RGBD video-emotion dataset. We propose a new supervised nonlinear multi-view laplacian eigenmaps (MvLE) approach and a multihidden-layer out-of-sample network (MHON) for RGB-D humanemotion recognition. To get better representations of RGB view and depth view, MvLE is"},"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":"1811.03478","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-08T15:03:58Z","cross_cats_sorted":[],"title_canon_sha256":"f25a148e8bd520a42305287dd94b7453b626104233d649ae001b675c92d8d962","abstract_canon_sha256":"9c68bdab7327c1135e9231081281e4977424d50dcdf827676b40be35f936347d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:16.024094Z","signature_b64":"p/mDpIjx6jmnMG2lcL999tzeSUhdiPTxWZ+DvIHpdBRRwooPJShksSjDmNr1fuANa02G20U7UnW+q5IkcUkHAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7cad95f0efbb5d012943f784ba42ec808903f11b3e9840770bdaf701af79b55f","last_reissued_at":"2026-05-18T00:01:16.023517Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:16.023517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-view Laplacian Eigenmaps Based on Bag-of-Neighbors For RGBD Human Emotion Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin Wang, Bixuan Du, Hong Qiao, Keye Zhang, Mingming Zhang, Shenglan Liu, Shuai Guo, Wenbo Luo, Yang Wang","submitted_at":"2018-11-08T15:03:58Z","abstract_excerpt":"Human emotion recognition is an important direction in the field of biometric and information forensics. However, most existing human emotion research are based on the single RGB view. In this paper, we introduce a RGBD video-emotion dataset and a RGBD face-emotion dataset for research. To our best knowledge, this may be the first RGBD video-emotion dataset. We propose a new supervised nonlinear multi-view laplacian eigenmaps (MvLE) approach and a multihidden-layer out-of-sample network (MHON) for RGB-D humanemotion recognition. To get better representations of RGB view and depth view, MvLE is"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.03478","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":"1811.03478","created_at":"2026-05-18T00:01:16.023597+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.03478v1","created_at":"2026-05-18T00:01:16.023597+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.03478","created_at":"2026-05-18T00:01:16.023597+00:00"},{"alias_kind":"pith_short_12","alias_value":"PSWZL4HPXNOQ","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"PSWZL4HPXNOQCKKD","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"PSWZL4HP","created_at":"2026-05-18T12:32:46.962924+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/PSWZL4HPXNOQCKKD66CLUQXMQC","json":"https://pith.science/pith/PSWZL4HPXNOQCKKD66CLUQXMQC.json","graph_json":"https://pith.science/api/pith-number/PSWZL4HPXNOQCKKD66CLUQXMQC/graph.json","events_json":"https://pith.science/api/pith-number/PSWZL4HPXNOQCKKD66CLUQXMQC/events.json","paper":"https://pith.science/paper/PSWZL4HP"},"agent_actions":{"view_html":"https://pith.science/pith/PSWZL4HPXNOQCKKD66CLUQXMQC","download_json":"https://pith.science/pith/PSWZL4HPXNOQCKKD66CLUQXMQC.json","view_paper":"https://pith.science/paper/PSWZL4HP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.03478&json=true","fetch_graph":"https://pith.science/api/pith-number/PSWZL4HPXNOQCKKD66CLUQXMQC/graph.json","fetch_events":"https://pith.science/api/pith-number/PSWZL4HPXNOQCKKD66CLUQXMQC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PSWZL4HPXNOQCKKD66CLUQXMQC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PSWZL4HPXNOQCKKD66CLUQXMQC/action/storage_attestation","attest_author":"https://pith.science/pith/PSWZL4HPXNOQCKKD66CLUQXMQC/action/author_attestation","sign_citation":"https://pith.science/pith/PSWZL4HPXNOQCKKD66CLUQXMQC/action/citation_signature","submit_replication":"https://pith.science/pith/PSWZL4HPXNOQCKKD66CLUQXMQC/action/replication_record"}},"created_at":"2026-05-18T00:01:16.023597+00:00","updated_at":"2026-05-18T00:01:16.023597+00:00"}