{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:Z6KPQOFEGKG3HD4EYL6663T2G3","short_pith_number":"pith:Z6KPQOFE","schema_version":"1.0","canonical_sha256":"cf94f838a4328db38f84c2fdef6e7a36cb2d274fffc9f0e71236d9d27389ab48","source":{"kind":"arxiv","id":"2103.10492","version":2},"attestation_state":"computed","paper":{"title":"Recent Advances in Deep Learning Techniques for Face Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Abdu Gumae, Awal Ahmed Fime, Delowar Sikder, Jakaria Rabbi, Mabrook S. Al-Rakhami, Md. Akil Raihan Iftee, Md. Nazrul Islam, Md. Tahmid Hasan Fuad, Mohtasim Fuad, Ovishake Sen","submitted_at":"2021-03-18T19:39:12Z","abstract_excerpt":"In recent years, researchers have proposed many deep learning (DL) methods for various tasks, and particularly face recognition (FR) made an enormous leap using these techniques. Deep FR systems benefit from the hierarchical architecture of the DL methods to learn discriminative face representation. Therefore, DL techniques significantly improve state-of-the-art performance on FR systems and encourage diverse and efficient real-world applications. In this paper, we present a comprehensive analysis of various FR systems that leverage the different types of DL techniques, and for the study, we s"},"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":"2103.10492","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-18T19:39:12Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"78501df6acb0ba789c3530cf47b4ac45653112be70822025438d3f58bdb2b5a1","abstract_canon_sha256":"5be2f156ab2f4ea466ba54bf22c95a442f6022de5c43c49e1551a92c80766654"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:59:47.797067Z","signature_b64":"y1IlZPdDc+iVopn6Qr5lKd9R+VJ1ztBCPMJ/qCiHS3ZRxwcIQNHJLUa/3xkpShu6fszNXoIhmeKqTj6N86CNAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf94f838a4328db38f84c2fdef6e7a36cb2d274fffc9f0e71236d9d27389ab48","last_reissued_at":"2026-07-05T02:59:47.796620Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:59:47.796620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Recent Advances in Deep Learning Techniques for Face Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Abdu Gumae, Awal Ahmed Fime, Delowar Sikder, Jakaria Rabbi, Mabrook S. Al-Rakhami, Md. Akil Raihan Iftee, Md. Nazrul Islam, Md. Tahmid Hasan Fuad, Mohtasim Fuad, Ovishake Sen","submitted_at":"2021-03-18T19:39:12Z","abstract_excerpt":"In recent years, researchers have proposed many deep learning (DL) methods for various tasks, and particularly face recognition (FR) made an enormous leap using these techniques. Deep FR systems benefit from the hierarchical architecture of the DL methods to learn discriminative face representation. Therefore, DL techniques significantly improve state-of-the-art performance on FR systems and encourage diverse and efficient real-world applications. In this paper, we present a comprehensive analysis of various FR systems that leverage the different types of DL techniques, and for the study, we s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.10492","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2103.10492/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2103.10492","created_at":"2026-07-05T02:59:47.796677+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.10492v2","created_at":"2026-07-05T02:59:47.796677+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.10492","created_at":"2026-07-05T02:59:47.796677+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z6KPQOFEGKG3","created_at":"2026-07-05T02:59:47.796677+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z6KPQOFEGKG3HD4E","created_at":"2026-07-05T02:59:47.796677+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z6KPQOFE","created_at":"2026-07-05T02:59:47.796677+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/Z6KPQOFEGKG3HD4EYL6663T2G3","json":"https://pith.science/pith/Z6KPQOFEGKG3HD4EYL6663T2G3.json","graph_json":"https://pith.science/api/pith-number/Z6KPQOFEGKG3HD4EYL6663T2G3/graph.json","events_json":"https://pith.science/api/pith-number/Z6KPQOFEGKG3HD4EYL6663T2G3/events.json","paper":"https://pith.science/paper/Z6KPQOFE"},"agent_actions":{"view_html":"https://pith.science/pith/Z6KPQOFEGKG3HD4EYL6663T2G3","download_json":"https://pith.science/pith/Z6KPQOFEGKG3HD4EYL6663T2G3.json","view_paper":"https://pith.science/paper/Z6KPQOFE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.10492&json=true","fetch_graph":"https://pith.science/api/pith-number/Z6KPQOFEGKG3HD4EYL6663T2G3/graph.json","fetch_events":"https://pith.science/api/pith-number/Z6KPQOFEGKG3HD4EYL6663T2G3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z6KPQOFEGKG3HD4EYL6663T2G3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z6KPQOFEGKG3HD4EYL6663T2G3/action/storage_attestation","attest_author":"https://pith.science/pith/Z6KPQOFEGKG3HD4EYL6663T2G3/action/author_attestation","sign_citation":"https://pith.science/pith/Z6KPQOFEGKG3HD4EYL6663T2G3/action/citation_signature","submit_replication":"https://pith.science/pith/Z6KPQOFEGKG3HD4EYL6663T2G3/action/replication_record"}},"created_at":"2026-07-05T02:59:47.796677+00:00","updated_at":"2026-07-05T02:59:47.796677+00:00"}