{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:LDPNNNOWYKHSUGVHOZKPCWKOND","short_pith_number":"pith:LDPNNNOW","canonical_record":{"source":{"id":"1703.03624","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-10T11:08:50Z","cross_cats_sorted":[],"title_canon_sha256":"b9b74a7043d3b826d2e6edc48ccd264c0e3c1b5a8eb371df0c1c45d3cc80a19f","abstract_canon_sha256":"7ed3ab7fb17a1e99ae0a4e427fef65bdbd093480cac1f534c07231f87d2121e7"},"schema_version":"1.0"},"canonical_sha256":"58ded6b5d6c28f2a1aa77654f1594e68d5c5b12ea644e52d3c325b1a503d975e","source":{"kind":"arxiv","id":"1703.03624","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.03624","created_at":"2026-05-18T00:48:58Z"},{"alias_kind":"arxiv_version","alias_value":"1703.03624v1","created_at":"2026-05-18T00:48:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.03624","created_at":"2026-05-18T00:48:58Z"},{"alias_kind":"pith_short_12","alias_value":"LDPNNNOWYKHS","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"LDPNNNOWYKHSUGVH","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"LDPNNNOW","created_at":"2026-05-18T12:31:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:LDPNNNOWYKHSUGVHOZKPCWKOND","target":"record","payload":{"canonical_record":{"source":{"id":"1703.03624","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-10T11:08:50Z","cross_cats_sorted":[],"title_canon_sha256":"b9b74a7043d3b826d2e6edc48ccd264c0e3c1b5a8eb371df0c1c45d3cc80a19f","abstract_canon_sha256":"7ed3ab7fb17a1e99ae0a4e427fef65bdbd093480cac1f534c07231f87d2121e7"},"schema_version":"1.0"},"canonical_sha256":"58ded6b5d6c28f2a1aa77654f1594e68d5c5b12ea644e52d3c325b1a503d975e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:48:58.505663Z","signature_b64":"0WvpdUr3y8XJUxyzZY63GRoSp3gMo9CHyToCbBPnCLrF9pKrAuGPdKyQ/3exsjJhA9+BpvpmKLEYBdjrICpbBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"58ded6b5d6c28f2a1aa77654f1594e68d5c5b12ea644e52d3c325b1a503d975e","last_reissued_at":"2026-05-18T00:48:58.504983Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:48:58.504983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.03624","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:48:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c+1lxut4q6XxH4jjwatwTWENfVbJNygdYtQfKgKWcK1zLqESY4uTmhGJCr5UHnZZ4bRBcub+s9WTUJt2//n2Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T14:35:41.012797Z"},"content_sha256":"abed68b48f65a0d6200e60c43dee1dffe8b9695662de97e7720024c03d9648c0","schema_version":"1.0","event_id":"sha256:abed68b48f65a0d6200e60c43dee1dffe8b9695662de97e7720024c03d9648c0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:LDPNNNOWYKHSUGVHOZKPCWKOND","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"From Depth Data to Head Pose Estimation: a Siamese approach","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":"2017-03-10T11:08:50Z","abstract_excerpt":"The correct estimation of the head pose is a problem of the great importance for many applications. For instance, it is an enabling technology in automotive for driver attention monitoring. In this paper, we tackle the pose estimation problem through a deep learning network working in regression manner. Traditional methods usually rely on visual facial features, such as facial landmarks or nose tip position. In contrast, we exploit a Convolutional Neural Network (CNN) to perform head pose estimation directly from depth data. We exploit a Siamese architecture and we propose a novel loss functio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.03624","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":""},"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":"2026-05-18T00:48:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FS7zxzM5QKE5qy2QZALy619Ga2+P+rAhII/leA0EdzOrJoen77s+PFpqcTe1e9i9tfY0RTfIDyyknU79YKi1Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T14:35:41.013145Z"},"content_sha256":"1076a1bc0e148a1f2b3778d75afc6eb0de654b2fcdb53eee0a29295a7240c0d3","schema_version":"1.0","event_id":"sha256:1076a1bc0e148a1f2b3778d75afc6eb0de654b2fcdb53eee0a29295a7240c0d3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LDPNNNOWYKHSUGVHOZKPCWKOND/bundle.json","state_url":"https://pith.science/pith/LDPNNNOWYKHSUGVHOZKPCWKOND/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LDPNNNOWYKHSUGVHOZKPCWKOND/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":"2026-06-05T14:35:41Z","links":{"resolver":"https://pith.science/pith/LDPNNNOWYKHSUGVHOZKPCWKOND","bundle":"https://pith.science/pith/LDPNNNOWYKHSUGVHOZKPCWKOND/bundle.json","state":"https://pith.science/pith/LDPNNNOWYKHSUGVHOZKPCWKOND/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LDPNNNOWYKHSUGVHOZKPCWKOND/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:LDPNNNOWYKHSUGVHOZKPCWKOND","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":"7ed3ab7fb17a1e99ae0a4e427fef65bdbd093480cac1f534c07231f87d2121e7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-10T11:08:50Z","title_canon_sha256":"b9b74a7043d3b826d2e6edc48ccd264c0e3c1b5a8eb371df0c1c45d3cc80a19f"},"schema_version":"1.0","source":{"id":"1703.03624","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.03624","created_at":"2026-05-18T00:48:58Z"},{"alias_kind":"arxiv_version","alias_value":"1703.03624v1","created_at":"2026-05-18T00:48:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.03624","created_at":"2026-05-18T00:48:58Z"},{"alias_kind":"pith_short_12","alias_value":"LDPNNNOWYKHS","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"LDPNNNOWYKHSUGVH","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"LDPNNNOW","created_at":"2026-05-18T12:31:28Z"}],"graph_snapshots":[{"event_id":"sha256:1076a1bc0e148a1f2b3778d75afc6eb0de654b2fcdb53eee0a29295a7240c0d3","target":"graph","created_at":"2026-05-18T00:48:58Z","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"},"paper":{"abstract_excerpt":"The correct estimation of the head pose is a problem of the great importance for many applications. For instance, it is an enabling technology in automotive for driver attention monitoring. In this paper, we tackle the pose estimation problem through a deep learning network working in regression manner. Traditional methods usually rely on visual facial features, such as facial landmarks or nose tip position. In contrast, we exploit a Convolutional Neural Network (CNN) to perform head pose estimation directly from depth data. We exploit a Siamese architecture and we propose a novel loss functio","authors_text":"Guido Borghi, Marco Venturelli, Rita Cucchiara, Roberto Vezzani","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-10T11:08:50Z","title":"From Depth Data to Head Pose Estimation: a Siamese approach"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.03624","kind":"arxiv","version":1},"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:abed68b48f65a0d6200e60c43dee1dffe8b9695662de97e7720024c03d9648c0","target":"record","created_at":"2026-05-18T00:48:58Z","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":"7ed3ab7fb17a1e99ae0a4e427fef65bdbd093480cac1f534c07231f87d2121e7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-03-10T11:08:50Z","title_canon_sha256":"b9b74a7043d3b826d2e6edc48ccd264c0e3c1b5a8eb371df0c1c45d3cc80a19f"},"schema_version":"1.0","source":{"id":"1703.03624","kind":"arxiv","version":1}},"canonical_sha256":"58ded6b5d6c28f2a1aa77654f1594e68d5c5b12ea644e52d3c325b1a503d975e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"58ded6b5d6c28f2a1aa77654f1594e68d5c5b12ea644e52d3c325b1a503d975e","first_computed_at":"2026-05-18T00:48:58.504983Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:48:58.504983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0WvpdUr3y8XJUxyzZY63GRoSp3gMo9CHyToCbBPnCLrF9pKrAuGPdKyQ/3exsjJhA9+BpvpmKLEYBdjrICpbBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:48:58.505663Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.03624","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:abed68b48f65a0d6200e60c43dee1dffe8b9695662de97e7720024c03d9648c0","sha256:1076a1bc0e148a1f2b3778d75afc6eb0de654b2fcdb53eee0a29295a7240c0d3"],"state_sha256":"6e0f77e501fa6c5995b4e6ee28b76e0af2d638175fa6f197dd0b56f49407dbcf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dAoeT3YTuaoJzJqu/iO3BhQfr+s7Awjc0VeOUM/0yMRIP3V7PfM04Y23VOfMDqDA0KIyOHknZdmAnxLKOhwpAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T14:35:41.015143Z","bundle_sha256":"421aa44b87b48a14356642003700de345d3dda63ef90f67783a317a37cf98d7f"}}