{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:J6FTPMDS6U67XENVWZ3NORJQBJ","short_pith_number":"pith:J6FTPMDS","schema_version":"1.0","canonical_sha256":"4f8b37b072f53dfb91b5b676d745300a61ec3ffd147a9cc1fb5eb1df9c9d37c5","source":{"kind":"arxiv","id":"1609.03892","version":1},"attestation_state":"computed","paper":{"title":"VIPLFaceNet: An Open Source Deep Face Recognition SDK","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Meina Kan, Shiguang Shan, Wanglong Wu, Xilin Chen, Xin Liu","submitted_at":"2016-09-13T15:13:55Z","abstract_excerpt":"Robust face representation is imperative to highly accurate face recognition. In this work, we propose an open source face recognition method with deep representation named as VIPLFaceNet, which is a 10-layer deep convolutional neural network with 7 convolutional layers and 3 fully-connected layers. Compared with the well-known AlexNet, our VIPLFaceNet takes only 20% training time and 60% testing time, but achieves 40\\% drop in error rate on the real-world face recognition benchmark LFW. Our VIPLFaceNet achieves 98.60% mean accuracy on LFW using one single network. An open-source C++ SDK based"},"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":"1609.03892","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-09-13T15:13:55Z","cross_cats_sorted":[],"title_canon_sha256":"7bc2e0d24b1c7e28c581dee3ea130ec099ffc937ae31ec4e1f62ea61863a1552","abstract_canon_sha256":"28aaee6b694fc9988a548b6c688c528f6ec1b12baf9e2260e2db9508432bd8ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:04:42.222932Z","signature_b64":"0Ev9zCvRlAv3a8I49YTXrV72Dwf6z45pT2fDvyJmPX7mOuX/4fU2SYOaAxwuVyLgMjW8yFYmGbCg0QzXjkIwDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f8b37b072f53dfb91b5b676d745300a61ec3ffd147a9cc1fb5eb1df9c9d37c5","last_reissued_at":"2026-05-18T01:04:42.222386Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:04:42.222386Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VIPLFaceNet: An Open Source Deep Face Recognition SDK","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Meina Kan, Shiguang Shan, Wanglong Wu, Xilin Chen, Xin Liu","submitted_at":"2016-09-13T15:13:55Z","abstract_excerpt":"Robust face representation is imperative to highly accurate face recognition. In this work, we propose an open source face recognition method with deep representation named as VIPLFaceNet, which is a 10-layer deep convolutional neural network with 7 convolutional layers and 3 fully-connected layers. Compared with the well-known AlexNet, our VIPLFaceNet takes only 20% training time and 60% testing time, but achieves 40\\% drop in error rate on the real-world face recognition benchmark LFW. Our VIPLFaceNet achieves 98.60% mean accuracy on LFW using one single network. An open-source C++ SDK based"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.03892","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":"1609.03892","created_at":"2026-05-18T01:04:42.222460+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.03892v1","created_at":"2026-05-18T01:04:42.222460+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.03892","created_at":"2026-05-18T01:04:42.222460+00:00"},{"alias_kind":"pith_short_12","alias_value":"J6FTPMDS6U67","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_16","alias_value":"J6FTPMDS6U67XENV","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_8","alias_value":"J6FTPMDS","created_at":"2026-05-18T12:30:22.444734+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/J6FTPMDS6U67XENVWZ3NORJQBJ","json":"https://pith.science/pith/J6FTPMDS6U67XENVWZ3NORJQBJ.json","graph_json":"https://pith.science/api/pith-number/J6FTPMDS6U67XENVWZ3NORJQBJ/graph.json","events_json":"https://pith.science/api/pith-number/J6FTPMDS6U67XENVWZ3NORJQBJ/events.json","paper":"https://pith.science/paper/J6FTPMDS"},"agent_actions":{"view_html":"https://pith.science/pith/J6FTPMDS6U67XENVWZ3NORJQBJ","download_json":"https://pith.science/pith/J6FTPMDS6U67XENVWZ3NORJQBJ.json","view_paper":"https://pith.science/paper/J6FTPMDS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.03892&json=true","fetch_graph":"https://pith.science/api/pith-number/J6FTPMDS6U67XENVWZ3NORJQBJ/graph.json","fetch_events":"https://pith.science/api/pith-number/J6FTPMDS6U67XENVWZ3NORJQBJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J6FTPMDS6U67XENVWZ3NORJQBJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J6FTPMDS6U67XENVWZ3NORJQBJ/action/storage_attestation","attest_author":"https://pith.science/pith/J6FTPMDS6U67XENVWZ3NORJQBJ/action/author_attestation","sign_citation":"https://pith.science/pith/J6FTPMDS6U67XENVWZ3NORJQBJ/action/citation_signature","submit_replication":"https://pith.science/pith/J6FTPMDS6U67XENVWZ3NORJQBJ/action/replication_record"}},"created_at":"2026-05-18T01:04:42.222460+00:00","updated_at":"2026-05-18T01:04:42.222460+00:00"}