{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:D3JZCZZIL7MXMQYGMG5TLQRVHL","short_pith_number":"pith:D3JZCZZI","schema_version":"1.0","canonical_sha256":"1ed39167285fd976430661bb35c2353ac931bc5c3f680920b12fd445110c9a01","source":{"kind":"arxiv","id":"2501.13432","version":3},"attestation_state":"computed","paper":{"title":"Emotion estimation from video footage with LSTM","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Samer Attrah","submitted_at":"2025-01-23T07:35:47Z","abstract_excerpt":"Emotion estimation in general is a field that has been studied for a long time, and several approaches exist using machine learning. in this paper, we present an LSTM model, that processes the blend-shapes produced by the library MediaPipe, for a face detected in a live stream of a camera, to estimate the main emotion from the facial expressions, this model is trained on the FER2013 dataset and delivers a result of 71% accuracy and 62% f1-score which meets the accuracy benchmark of the FER2013 dataset, with significantly reduced computation costs. https://github.com/Samir-atra/Emotion_estimati"},"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":"2501.13432","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-23T07:35:47Z","cross_cats_sorted":["cs.LG","cs.RO"],"title_canon_sha256":"c377b8705f3e0997b640ba3f4f6642b49313f8e87a2eb6d52cc3f89779c5845d","abstract_canon_sha256":"5dbf4478278e3f9910bf0e0da99991cbc74114c6dcc4bc94eb08b9f56fa09b77"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:43:04.480039Z","signature_b64":"7+2c93GHaborVM3cTBSlNy7Tqvo92yhVhwb+Jg+pSviMOVWUT2rGz0E95j4J8IgDJnDnVAYA5mRiAzr204PPAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1ed39167285fd976430661bb35c2353ac931bc5c3f680920b12fd445110c9a01","last_reissued_at":"2026-07-05T10:43:04.479510Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:43:04.479510Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Emotion estimation from video footage with LSTM","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Samer Attrah","submitted_at":"2025-01-23T07:35:47Z","abstract_excerpt":"Emotion estimation in general is a field that has been studied for a long time, and several approaches exist using machine learning. in this paper, we present an LSTM model, that processes the blend-shapes produced by the library MediaPipe, for a face detected in a live stream of a camera, to estimate the main emotion from the facial expressions, this model is trained on the FER2013 dataset and delivers a result of 71% accuracy and 62% f1-score which meets the accuracy benchmark of the FER2013 dataset, with significantly reduced computation costs. https://github.com/Samir-atra/Emotion_estimati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13432","kind":"arxiv","version":3},"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/2501.13432/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":"2501.13432","created_at":"2026-07-05T10:43:04.479569+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.13432v3","created_at":"2026-07-05T10:43:04.479569+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13432","created_at":"2026-07-05T10:43:04.479569+00:00"},{"alias_kind":"pith_short_12","alias_value":"D3JZCZZIL7MX","created_at":"2026-07-05T10:43:04.479569+00:00"},{"alias_kind":"pith_short_16","alias_value":"D3JZCZZIL7MXMQYG","created_at":"2026-07-05T10:43:04.479569+00:00"},{"alias_kind":"pith_short_8","alias_value":"D3JZCZZI","created_at":"2026-07-05T10:43:04.479569+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/D3JZCZZIL7MXMQYGMG5TLQRVHL","json":"https://pith.science/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL.json","graph_json":"https://pith.science/api/pith-number/D3JZCZZIL7MXMQYGMG5TLQRVHL/graph.json","events_json":"https://pith.science/api/pith-number/D3JZCZZIL7MXMQYGMG5TLQRVHL/events.json","paper":"https://pith.science/paper/D3JZCZZI"},"agent_actions":{"view_html":"https://pith.science/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL","download_json":"https://pith.science/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL.json","view_paper":"https://pith.science/paper/D3JZCZZI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.13432&json=true","fetch_graph":"https://pith.science/api/pith-number/D3JZCZZIL7MXMQYGMG5TLQRVHL/graph.json","fetch_events":"https://pith.science/api/pith-number/D3JZCZZIL7MXMQYGMG5TLQRVHL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL/action/storage_attestation","attest_author":"https://pith.science/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL/action/author_attestation","sign_citation":"https://pith.science/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL/action/citation_signature","submit_replication":"https://pith.science/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL/action/replication_record"}},"created_at":"2026-07-05T10:43:04.479569+00:00","updated_at":"2026-07-05T10:43:04.479569+00:00"}