{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2010:UUQ4QR6VXKTTKTMEC2HXDO6K6L","short_pith_number":"pith:UUQ4QR6V","schema_version":"1.0","canonical_sha256":"a521c847d5baa7354d84168f71bbcaf2d48a6e06167b17de7b356be5d53564ab","source":{"kind":"arxiv","id":"1004.0378","version":7},"attestation_state":"computed","paper":{"title":"Facial Expression Representation and Recognition Using 2DHLDA, Gabor Wavelets, and Ensemble Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Mahmoud Khademi, Mehran Safayani, Mohammad H. Kiapour, Mohammad T. Manzuri, M. Shojaei","submitted_at":"2010-04-02T19:26:47Z","abstract_excerpt":"In this paper, a novel method for representation and recognition of the facial expressions in two-dimensional image sequences is presented. We apply a variation of two-dimensional heteroscedastic linear discriminant analysis (2DHLDA) algorithm, as an efficient dimensionality reduction technique, to Gabor representation of the input sequence. 2DHLDA is an extension of the two-dimensional linear discriminant analysis (2DLDA) approach and it removes the equal within-class covariance. By applying 2DHLDA in two directions, we eliminate the correlations between both image columns and image rows. The"},"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":"1004.0378","kind":"arxiv","version":7},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2010-04-02T19:26:47Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"5da113d475e4d59bd813363b86de05dc81a24391aac0eb6d07051304b88fe61e","abstract_canon_sha256":"6c8b88c1c2cf9e0e38e7f85f96ccdd0e3e55968599850bd4b6e3e424173fec3e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:50:35.761444Z","signature_b64":"HGyh4tbrnxm71ClH3cwapEbMI7/n6+l563Wkz3xyA8qgcgOnwgsoJZpTMw0wMwzpVCnarWlwH4JAYzh9zA5uBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a521c847d5baa7354d84168f71bbcaf2d48a6e06167b17de7b356be5d53564ab","last_reissued_at":"2026-05-18T03:50:35.760851Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:50:35.760851Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Facial Expression Representation and Recognition Using 2DHLDA, Gabor Wavelets, and Ensemble Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Mahmoud Khademi, Mehran Safayani, Mohammad H. Kiapour, Mohammad T. Manzuri, M. Shojaei","submitted_at":"2010-04-02T19:26:47Z","abstract_excerpt":"In this paper, a novel method for representation and recognition of the facial expressions in two-dimensional image sequences is presented. We apply a variation of two-dimensional heteroscedastic linear discriminant analysis (2DHLDA) algorithm, as an efficient dimensionality reduction technique, to Gabor representation of the input sequence. 2DHLDA is an extension of the two-dimensional linear discriminant analysis (2DLDA) approach and it removes the equal within-class covariance. By applying 2DHLDA in two directions, we eliminate the correlations between both image columns and image rows. The"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1004.0378","kind":"arxiv","version":7},"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":"1004.0378","created_at":"2026-05-18T03:50:35.760929+00:00"},{"alias_kind":"arxiv_version","alias_value":"1004.0378v7","created_at":"2026-05-18T03:50:35.760929+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1004.0378","created_at":"2026-05-18T03:50:35.760929+00:00"},{"alias_kind":"pith_short_12","alias_value":"UUQ4QR6VXKTT","created_at":"2026-05-18T12:26:15.391820+00:00"},{"alias_kind":"pith_short_16","alias_value":"UUQ4QR6VXKTTKTME","created_at":"2026-05-18T12:26:15.391820+00:00"},{"alias_kind":"pith_short_8","alias_value":"UUQ4QR6V","created_at":"2026-05-18T12:26:15.391820+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/UUQ4QR6VXKTTKTMEC2HXDO6K6L","json":"https://pith.science/pith/UUQ4QR6VXKTTKTMEC2HXDO6K6L.json","graph_json":"https://pith.science/api/pith-number/UUQ4QR6VXKTTKTMEC2HXDO6K6L/graph.json","events_json":"https://pith.science/api/pith-number/UUQ4QR6VXKTTKTMEC2HXDO6K6L/events.json","paper":"https://pith.science/paper/UUQ4QR6V"},"agent_actions":{"view_html":"https://pith.science/pith/UUQ4QR6VXKTTKTMEC2HXDO6K6L","download_json":"https://pith.science/pith/UUQ4QR6VXKTTKTMEC2HXDO6K6L.json","view_paper":"https://pith.science/paper/UUQ4QR6V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1004.0378&json=true","fetch_graph":"https://pith.science/api/pith-number/UUQ4QR6VXKTTKTMEC2HXDO6K6L/graph.json","fetch_events":"https://pith.science/api/pith-number/UUQ4QR6VXKTTKTMEC2HXDO6K6L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UUQ4QR6VXKTTKTMEC2HXDO6K6L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UUQ4QR6VXKTTKTMEC2HXDO6K6L/action/storage_attestation","attest_author":"https://pith.science/pith/UUQ4QR6VXKTTKTMEC2HXDO6K6L/action/author_attestation","sign_citation":"https://pith.science/pith/UUQ4QR6VXKTTKTMEC2HXDO6K6L/action/citation_signature","submit_replication":"https://pith.science/pith/UUQ4QR6VXKTTKTMEC2HXDO6K6L/action/replication_record"}},"created_at":"2026-05-18T03:50:35.760929+00:00","updated_at":"2026-05-18T03:50:35.760929+00:00"}