{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:LFSGRTVICUXEVWPIMU4XVHOUSZ","short_pith_number":"pith:LFSGRTVI","schema_version":"1.0","canonical_sha256":"596468cea8152e4ad9e865397a9dd49659f336273c14f30311e6c5fdb4e6b41b","source":{"kind":"arxiv","id":"1611.10195","version":3},"attestation_state":"computed","paper":{"title":"POSEidon: Face-from-Depth for Driver Pose Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guido Borghi, Marco Venturelli, Rita Cucchiara, Roberto Vezzani","submitted_at":"2016-11-30T14:57:06Z","abstract_excerpt":"Fast and accurate upper-body and head pose estimation is a key task for automatic monitoring of driver attention, a challenging context characterized by severe illumination changes, occlusions and extreme poses. In this work, we present a new deep learning framework for head localization and pose estimation on depth images. The core of the proposal is a regression neural network, called POSEidon, which is composed of three independent convolutional nets followed by a fusion layer, specially conceived for understanding the pose by depth. In addition, to recover the intrinsic value of face appea"},"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":"1611.10195","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-30T14:57:06Z","cross_cats_sorted":[],"title_canon_sha256":"956cc64ec67a5aeb35fd12c020ebe22ecd1ce26a3ccc47285ae808a840f6ca51","abstract_canon_sha256":"b8973b6650f7d198a2b36ac43e6f4f1ccc3b5e46864081453f0fa6c20a574bfd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:07.968161Z","signature_b64":"uzc7d6uMtJqzqQ14xdyQAO2c0R+mMLofarMq6At1VMDl9txWY7S3Cy5FpoPV7eM0/qCViIzUnHB4tLpJUNE/BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"596468cea8152e4ad9e865397a9dd49659f336273c14f30311e6c5fdb4e6b41b","last_reissued_at":"2026-05-18T00:28:07.967484Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:07.967484Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"POSEidon: Face-from-Depth for Driver Pose Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guido Borghi, Marco Venturelli, Rita Cucchiara, Roberto Vezzani","submitted_at":"2016-11-30T14:57:06Z","abstract_excerpt":"Fast and accurate upper-body and head pose estimation is a key task for automatic monitoring of driver attention, a challenging context characterized by severe illumination changes, occlusions and extreme poses. In this work, we present a new deep learning framework for head localization and pose estimation on depth images. The core of the proposal is a regression neural network, called POSEidon, which is composed of three independent convolutional nets followed by a fusion layer, specially conceived for understanding the pose by depth. In addition, to recover the intrinsic value of face appea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.10195","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":""},"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":"1611.10195","created_at":"2026-05-18T00:28:07.967592+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.10195v3","created_at":"2026-05-18T00:28:07.967592+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.10195","created_at":"2026-05-18T00:28:07.967592+00:00"},{"alias_kind":"pith_short_12","alias_value":"LFSGRTVICUXE","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_16","alias_value":"LFSGRTVICUXEVWPI","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_8","alias_value":"LFSGRTVI","created_at":"2026-05-18T12:30:29.479603+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/LFSGRTVICUXEVWPIMU4XVHOUSZ","json":"https://pith.science/pith/LFSGRTVICUXEVWPIMU4XVHOUSZ.json","graph_json":"https://pith.science/api/pith-number/LFSGRTVICUXEVWPIMU4XVHOUSZ/graph.json","events_json":"https://pith.science/api/pith-number/LFSGRTVICUXEVWPIMU4XVHOUSZ/events.json","paper":"https://pith.science/paper/LFSGRTVI"},"agent_actions":{"view_html":"https://pith.science/pith/LFSGRTVICUXEVWPIMU4XVHOUSZ","download_json":"https://pith.science/pith/LFSGRTVICUXEVWPIMU4XVHOUSZ.json","view_paper":"https://pith.science/paper/LFSGRTVI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.10195&json=true","fetch_graph":"https://pith.science/api/pith-number/LFSGRTVICUXEVWPIMU4XVHOUSZ/graph.json","fetch_events":"https://pith.science/api/pith-number/LFSGRTVICUXEVWPIMU4XVHOUSZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LFSGRTVICUXEVWPIMU4XVHOUSZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LFSGRTVICUXEVWPIMU4XVHOUSZ/action/storage_attestation","attest_author":"https://pith.science/pith/LFSGRTVICUXEVWPIMU4XVHOUSZ/action/author_attestation","sign_citation":"https://pith.science/pith/LFSGRTVICUXEVWPIMU4XVHOUSZ/action/citation_signature","submit_replication":"https://pith.science/pith/LFSGRTVICUXEVWPIMU4XVHOUSZ/action/replication_record"}},"created_at":"2026-05-18T00:28:07.967592+00:00","updated_at":"2026-05-18T00:28:07.967592+00:00"}