{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:6ZGBK2646CPDWGAMZCSZCKUZRN","short_pith_number":"pith:6ZGBK264","schema_version":"1.0","canonical_sha256":"f64c156bdcf09e3b180cc8a5912a998b6f0017ca2601d2572caff91453fbf64b","source":{"kind":"arxiv","id":"1708.02412","version":1},"attestation_state":"computed","paper":{"title":"Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ran He, Tieniu Tan, Xiang Wu, Zhenan Sun","submitted_at":"2017-08-08T09:07:34Z","abstract_excerpt":"Heterogeneous face recognition (HFR) aims to match facial images acquired from different sensing modalities with mission-critical applications in forensics, security and commercial sectors. However, HFR is a much more challenging problem than traditional face recognition because of large intra-class variations of heterogeneous face images and limited training samples of cross-modality face image pairs. This paper proposes a novel approach namely Wasserstein CNN (convolutional neural networks, or WCNN for short) to learn invariant features between near-infrared and visual face images (i.e. NIR-"},"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":"1708.02412","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-08-08T09:07:34Z","cross_cats_sorted":[],"title_canon_sha256":"ad8834718d7e29de38f9c12eff307ea618b52a5c36b938cd1634dd2609f11dc9","abstract_canon_sha256":"0a9500c95cd419ea81e7a5ce8315874bf2d34aee4c22291265a386ec34874319"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:38:24.361784Z","signature_b64":"Q3lQqx/x0IIofMdwYC1Djh8Bf0uUaB4FjdElWmJIQMawF2EqOXzWsGALvco3g2EOKqLoXbWbc7vzKH/rGjSnDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f64c156bdcf09e3b180cc8a5912a998b6f0017ca2601d2572caff91453fbf64b","last_reissued_at":"2026-05-18T00:38:24.361049Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:38:24.361049Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ran He, Tieniu Tan, Xiang Wu, Zhenan Sun","submitted_at":"2017-08-08T09:07:34Z","abstract_excerpt":"Heterogeneous face recognition (HFR) aims to match facial images acquired from different sensing modalities with mission-critical applications in forensics, security and commercial sectors. However, HFR is a much more challenging problem than traditional face recognition because of large intra-class variations of heterogeneous face images and limited training samples of cross-modality face image pairs. This paper proposes a novel approach namely Wasserstein CNN (convolutional neural networks, or WCNN for short) to learn invariant features between near-infrared and visual face images (i.e. NIR-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.02412","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1708.02412","created_at":"2026-05-18T00:38:24.361176+00:00"},{"alias_kind":"arxiv_version","alias_value":"1708.02412v1","created_at":"2026-05-18T00:38:24.361176+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.02412","created_at":"2026-05-18T00:38:24.361176+00:00"},{"alias_kind":"pith_short_12","alias_value":"6ZGBK2646CPD","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_16","alias_value":"6ZGBK2646CPDWGAM","created_at":"2026-05-18T12:31:03.183658+00:00"},{"alias_kind":"pith_short_8","alias_value":"6ZGBK264","created_at":"2026-05-18T12:31:03.183658+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/6ZGBK2646CPDWGAMZCSZCKUZRN","json":"https://pith.science/pith/6ZGBK2646CPDWGAMZCSZCKUZRN.json","graph_json":"https://pith.science/api/pith-number/6ZGBK2646CPDWGAMZCSZCKUZRN/graph.json","events_json":"https://pith.science/api/pith-number/6ZGBK2646CPDWGAMZCSZCKUZRN/events.json","paper":"https://pith.science/paper/6ZGBK264"},"agent_actions":{"view_html":"https://pith.science/pith/6ZGBK2646CPDWGAMZCSZCKUZRN","download_json":"https://pith.science/pith/6ZGBK2646CPDWGAMZCSZCKUZRN.json","view_paper":"https://pith.science/paper/6ZGBK264","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1708.02412&json=true","fetch_graph":"https://pith.science/api/pith-number/6ZGBK2646CPDWGAMZCSZCKUZRN/graph.json","fetch_events":"https://pith.science/api/pith-number/6ZGBK2646CPDWGAMZCSZCKUZRN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6ZGBK2646CPDWGAMZCSZCKUZRN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6ZGBK2646CPDWGAMZCSZCKUZRN/action/storage_attestation","attest_author":"https://pith.science/pith/6ZGBK2646CPDWGAMZCSZCKUZRN/action/author_attestation","sign_citation":"https://pith.science/pith/6ZGBK2646CPDWGAMZCSZCKUZRN/action/citation_signature","submit_replication":"https://pith.science/pith/6ZGBK2646CPDWGAMZCSZCKUZRN/action/replication_record"}},"created_at":"2026-05-18T00:38:24.361176+00:00","updated_at":"2026-05-18T00:38:24.361176+00:00"}