{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:NLTSWZWOK2I3WUCCEXS2CP5VTN","short_pith_number":"pith:NLTSWZWO","schema_version":"1.0","canonical_sha256":"6ae72b66ce5691bb504225e5a13fb59b4bc0ce35aef9d33ff2e25b61d79e0296","source":{"kind":"arxiv","id":"1704.02402","version":2},"attestation_state":"computed","paper":{"title":"GoDP: Globally optimized dual pathway system for facial landmark localization in-the-wild","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ioannis A. Kakadiaris, Shishir K. Shah, Yuhang Wu","submitted_at":"2017-04-07T23:39:29Z","abstract_excerpt":"Facial landmark localization is a fundamental module for pose-invariant face recognition. The most common approach for facial landmark detection is cascaded regression, which is composed of two steps: feature extraction and facial shape regression. Recent methods employ deep convolutional networks to extract robust features for each step, while the whole system could be regarded as a deep cascaded regression architecture. In this work, instead of employing a deep regression network, a Globally Optimized Dual-Pathway (GoDP) deep architecture is proposed to identify the target pixels through sol"},"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":"1704.02402","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-04-07T23:39:29Z","cross_cats_sorted":[],"title_canon_sha256":"189885f1d9968dd69bc48d1fddfe76e170defd4aeab1600117b5eae62e0558b4","abstract_canon_sha256":"cd20f8689ac72b58f878695bf4c44d5621d60a3e5fa05563baa76d2ab0d15c06"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:35.819920Z","signature_b64":"mWkmqnXLnQ7oEzNXdAcz9yA6lHV3BCEHLt+zJ8G8ccE8jSjGOWo0ccFlYTGioQlZ1WwyFbWFAecMXS+yjLqSCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ae72b66ce5691bb504225e5a13fb59b4bc0ce35aef9d33ff2e25b61d79e0296","last_reissued_at":"2026-05-18T00:19:35.819385Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:35.819385Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GoDP: Globally optimized dual pathway system for facial landmark localization in-the-wild","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ioannis A. Kakadiaris, Shishir K. Shah, Yuhang Wu","submitted_at":"2017-04-07T23:39:29Z","abstract_excerpt":"Facial landmark localization is a fundamental module for pose-invariant face recognition. The most common approach for facial landmark detection is cascaded regression, which is composed of two steps: feature extraction and facial shape regression. Recent methods employ deep convolutional networks to extract robust features for each step, while the whole system could be regarded as a deep cascaded regression architecture. In this work, instead of employing a deep regression network, a Globally Optimized Dual-Pathway (GoDP) deep architecture is proposed to identify the target pixels through sol"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.02402","kind":"arxiv","version":2},"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":"1704.02402","created_at":"2026-05-18T00:19:35.819467+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.02402v2","created_at":"2026-05-18T00:19:35.819467+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.02402","created_at":"2026-05-18T00:19:35.819467+00:00"},{"alias_kind":"pith_short_12","alias_value":"NLTSWZWOK2I3","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_16","alias_value":"NLTSWZWOK2I3WUCC","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_8","alias_value":"NLTSWZWO","created_at":"2026-05-18T12:31:31.346846+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/NLTSWZWOK2I3WUCCEXS2CP5VTN","json":"https://pith.science/pith/NLTSWZWOK2I3WUCCEXS2CP5VTN.json","graph_json":"https://pith.science/api/pith-number/NLTSWZWOK2I3WUCCEXS2CP5VTN/graph.json","events_json":"https://pith.science/api/pith-number/NLTSWZWOK2I3WUCCEXS2CP5VTN/events.json","paper":"https://pith.science/paper/NLTSWZWO"},"agent_actions":{"view_html":"https://pith.science/pith/NLTSWZWOK2I3WUCCEXS2CP5VTN","download_json":"https://pith.science/pith/NLTSWZWOK2I3WUCCEXS2CP5VTN.json","view_paper":"https://pith.science/paper/NLTSWZWO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.02402&json=true","fetch_graph":"https://pith.science/api/pith-number/NLTSWZWOK2I3WUCCEXS2CP5VTN/graph.json","fetch_events":"https://pith.science/api/pith-number/NLTSWZWOK2I3WUCCEXS2CP5VTN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NLTSWZWOK2I3WUCCEXS2CP5VTN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NLTSWZWOK2I3WUCCEXS2CP5VTN/action/storage_attestation","attest_author":"https://pith.science/pith/NLTSWZWOK2I3WUCCEXS2CP5VTN/action/author_attestation","sign_citation":"https://pith.science/pith/NLTSWZWOK2I3WUCCEXS2CP5VTN/action/citation_signature","submit_replication":"https://pith.science/pith/NLTSWZWOK2I3WUCCEXS2CP5VTN/action/replication_record"}},"created_at":"2026-05-18T00:19:35.819467+00:00","updated_at":"2026-05-18T00:19:35.819467+00:00"}