{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:D3JZCZZIL7MXMQYGMG5TLQRVHL","short_pith_number":"pith:D3JZCZZI","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"},"canonical_sha256":"1ed39167285fd976430661bb35c2353ac931bc5c3f680920b12fd445110c9a01","source":{"kind":"arxiv","id":"2501.13432","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.13432","created_at":"2026-07-05T10:43:04Z"},{"alias_kind":"arxiv_version","alias_value":"2501.13432v3","created_at":"2026-07-05T10:43:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13432","created_at":"2026-07-05T10:43:04Z"},{"alias_kind":"pith_short_12","alias_value":"D3JZCZZIL7MX","created_at":"2026-07-05T10:43:04Z"},{"alias_kind":"pith_short_16","alias_value":"D3JZCZZIL7MXMQYG","created_at":"2026-07-05T10:43:04Z"},{"alias_kind":"pith_short_8","alias_value":"D3JZCZZI","created_at":"2026-07-05T10:43:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:D3JZCZZIL7MXMQYGMG5TLQRVHL","target":"record","payload":{"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"},"canonical_sha256":"1ed39167285fd976430661bb35c2353ac931bc5c3f680920b12fd445110c9a01","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"},"source_kind":"arxiv","source_id":"2501.13432","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:43:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8Zq3opm82sJgUaLDypsfPXqp7posfa40q7FoNZ0RXqq0LZ3rRBJGeJtZTELK8bM5VxGd55BIqlw7bdGxid+xCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T18:54:21.804109Z"},"content_sha256":"dae2389bdcde9e9405529c6934371a7bb102eb8f429cbf670e005399d5705022","schema_version":"1.0","event_id":"sha256:dae2389bdcde9e9405529c6934371a7bb102eb8f429cbf670e005399d5705022"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:D3JZCZZIL7MXMQYGMG5TLQRVHL","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:43:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ppf5hH85v5Lpu7ZmTCK6H1uYXlfvDpV98rjfIoZ4E13idyjLFIFSqEtO6dwSH4spHK8Gj+5hHLJIgQ6Nh2o+CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T18:54:21.804493Z"},"content_sha256":"31dba026c6697f5a86cad4617100320cb5fa926dd0257c873a4744cbf814aaab","schema_version":"1.0","event_id":"sha256:31dba026c6697f5a86cad4617100320cb5fa926dd0257c873a4744cbf814aaab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL/bundle.json","state_url":"https://pith.science/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-19T18:54:21Z","links":{"resolver":"https://pith.science/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL","bundle":"https://pith.science/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL/bundle.json","state":"https://pith.science/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D3JZCZZIL7MXMQYGMG5TLQRVHL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:D3JZCZZIL7MXMQYGMG5TLQRVHL","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"5dbf4478278e3f9910bf0e0da99991cbc74114c6dcc4bc94eb08b9f56fa09b77","cross_cats_sorted":["cs.LG","cs.RO"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-23T07:35:47Z","title_canon_sha256":"c377b8705f3e0997b640ba3f4f6642b49313f8e87a2eb6d52cc3f89779c5845d"},"schema_version":"1.0","source":{"id":"2501.13432","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.13432","created_at":"2026-07-05T10:43:04Z"},{"alias_kind":"arxiv_version","alias_value":"2501.13432v3","created_at":"2026-07-05T10:43:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.13432","created_at":"2026-07-05T10:43:04Z"},{"alias_kind":"pith_short_12","alias_value":"D3JZCZZIL7MX","created_at":"2026-07-05T10:43:04Z"},{"alias_kind":"pith_short_16","alias_value":"D3JZCZZIL7MXMQYG","created_at":"2026-07-05T10:43:04Z"},{"alias_kind":"pith_short_8","alias_value":"D3JZCZZI","created_at":"2026-07-05T10:43:04Z"}],"graph_snapshots":[{"event_id":"sha256:31dba026c6697f5a86cad4617100320cb5fa926dd0257c873a4744cbf814aaab","target":"graph","created_at":"2026-07-05T10:43:04Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2501.13432/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"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","authors_text":"Samer Attrah","cross_cats":["cs.LG","cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-23T07:35:47Z","title":"Emotion estimation from video footage with LSTM"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.13432","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:dae2389bdcde9e9405529c6934371a7bb102eb8f429cbf670e005399d5705022","target":"record","created_at":"2026-07-05T10:43:04Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"5dbf4478278e3f9910bf0e0da99991cbc74114c6dcc4bc94eb08b9f56fa09b77","cross_cats_sorted":["cs.LG","cs.RO"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-23T07:35:47Z","title_canon_sha256":"c377b8705f3e0997b640ba3f4f6642b49313f8e87a2eb6d52cc3f89779c5845d"},"schema_version":"1.0","source":{"id":"2501.13432","kind":"arxiv","version":3}},"canonical_sha256":"1ed39167285fd976430661bb35c2353ac931bc5c3f680920b12fd445110c9a01","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1ed39167285fd976430661bb35c2353ac931bc5c3f680920b12fd445110c9a01","first_computed_at":"2026-07-05T10:43:04.479510Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:43:04.479510Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7+2c93GHaborVM3cTBSlNy7Tqvo92yhVhwb+Jg+pSviMOVWUT2rGz0E95j4J8IgDJnDnVAYA5mRiAzr204PPAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:43:04.480039Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.13432","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dae2389bdcde9e9405529c6934371a7bb102eb8f429cbf670e005399d5705022","sha256:31dba026c6697f5a86cad4617100320cb5fa926dd0257c873a4744cbf814aaab"],"state_sha256":"0b2bbb50922a3a278164cc6da856cf1fe94f2fe4c505532d7c0a419712ddbb0d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z7ogc62HuPiIgaYHNej+q7KyKq7f7eOJAmlXWB2BFSYEI/OCkE9z+JZxyVGpmi5ekR5vhupEpazsiEWl9QppAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T18:54:21.806832Z","bundle_sha256":"9f8b012b55f845ec55c3f2e4cf3dbaf61e87e9b1f740daaf39f773df8eabd427"}}