{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:M2DJSOMYRZXCF7IZCYALIIOOO2","short_pith_number":"pith:M2DJSOMY","schema_version":"1.0","canonical_sha256":"66869939988e6e22fd191600b421ce76840946a213415ca6f6c7dd26bb1428b0","source":{"kind":"arxiv","id":"2005.06198","version":1},"attestation_state":"computed","paper":{"title":"Mean Oriented Riesz Features for Micro Expression Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anne-Claire Legrand, Carlos Arango Duque, Hubert Konik, Olivier Alata, R\\'emi Emonet","submitted_at":"2020-05-13T08:23:34Z","abstract_excerpt":"Micro-expressions are brief and subtle facial expressions that go on and off the face in a fraction of a second. This kind of facial expressions usually occurs in high stake situations and is considered to reflect a human's real intent. There has been some interest in micro-expression analysis, however, a great majority of the methods are based on classically established computer vision methods such as local binary patterns, histogram of gradients and optical flow. A novel methodology for micro-expression recognition using the Riesz pyramid, a multi-scale steerable Hilbert transform is present"},"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":"2005.06198","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-05-13T08:23:34Z","cross_cats_sorted":[],"title_canon_sha256":"f0bb88d5b75a1ebd7c8a4c7ced99869596414608a7f0a36be6f299f6632a8a06","abstract_canon_sha256":"2be31d88978ce03146afec79fa104dffd87f7f63ca8aa03121b9612fb5fc0332"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:02:33.064592Z","signature_b64":"Gor0NV/1Bsn3z9XT/xwagenGiv3cLpN2yCj1xbUgROiuKDc+KvkUlWcbMWKTgzeAnrVXnAS58bVl/0UYzxeFCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"66869939988e6e22fd191600b421ce76840946a213415ca6f6c7dd26bb1428b0","last_reissued_at":"2026-07-05T01:02:33.064147Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:02:33.064147Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Mean Oriented Riesz Features for Micro Expression Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anne-Claire Legrand, Carlos Arango Duque, Hubert Konik, Olivier Alata, R\\'emi Emonet","submitted_at":"2020-05-13T08:23:34Z","abstract_excerpt":"Micro-expressions are brief and subtle facial expressions that go on and off the face in a fraction of a second. This kind of facial expressions usually occurs in high stake situations and is considered to reflect a human's real intent. There has been some interest in micro-expression analysis, however, a great majority of the methods are based on classically established computer vision methods such as local binary patterns, histogram of gradients and optical flow. A novel methodology for micro-expression recognition using the Riesz pyramid, a multi-scale steerable Hilbert transform is present"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.06198","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2005.06198/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2005.06198","created_at":"2026-07-05T01:02:33.064202+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.06198v1","created_at":"2026-07-05T01:02:33.064202+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.06198","created_at":"2026-07-05T01:02:33.064202+00:00"},{"alias_kind":"pith_short_12","alias_value":"M2DJSOMYRZXC","created_at":"2026-07-05T01:02:33.064202+00:00"},{"alias_kind":"pith_short_16","alias_value":"M2DJSOMYRZXCF7IZ","created_at":"2026-07-05T01:02:33.064202+00:00"},{"alias_kind":"pith_short_8","alias_value":"M2DJSOMY","created_at":"2026-07-05T01:02:33.064202+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/M2DJSOMYRZXCF7IZCYALIIOOO2","json":"https://pith.science/pith/M2DJSOMYRZXCF7IZCYALIIOOO2.json","graph_json":"https://pith.science/api/pith-number/M2DJSOMYRZXCF7IZCYALIIOOO2/graph.json","events_json":"https://pith.science/api/pith-number/M2DJSOMYRZXCF7IZCYALIIOOO2/events.json","paper":"https://pith.science/paper/M2DJSOMY"},"agent_actions":{"view_html":"https://pith.science/pith/M2DJSOMYRZXCF7IZCYALIIOOO2","download_json":"https://pith.science/pith/M2DJSOMYRZXCF7IZCYALIIOOO2.json","view_paper":"https://pith.science/paper/M2DJSOMY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.06198&json=true","fetch_graph":"https://pith.science/api/pith-number/M2DJSOMYRZXCF7IZCYALIIOOO2/graph.json","fetch_events":"https://pith.science/api/pith-number/M2DJSOMYRZXCF7IZCYALIIOOO2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M2DJSOMYRZXCF7IZCYALIIOOO2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M2DJSOMYRZXCF7IZCYALIIOOO2/action/storage_attestation","attest_author":"https://pith.science/pith/M2DJSOMYRZXCF7IZCYALIIOOO2/action/author_attestation","sign_citation":"https://pith.science/pith/M2DJSOMYRZXCF7IZCYALIIOOO2/action/citation_signature","submit_replication":"https://pith.science/pith/M2DJSOMYRZXCF7IZCYALIIOOO2/action/replication_record"}},"created_at":"2026-07-05T01:02:33.064202+00:00","updated_at":"2026-07-05T01:02:33.064202+00:00"}