{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:PC2DYR4IL2C3ZGKET6IVP74EQB","short_pith_number":"pith:PC2DYR4I","canonical_record":{"source":{"id":"1811.04346","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-11-11T04:17:10Z","cross_cats_sorted":[],"title_canon_sha256":"e09ef08155e894e283ef280f06fd7062893fcbdfd3b6c2fe3b04825f2067d2a9","abstract_canon_sha256":"c122b9673ca0e98009c3a74a55ed93c79f7a50b9894d12a0090cb57df4a60054"},"schema_version":"1.0"},"canonical_sha256":"78b43c47885e85bc99449f9157ff848064d93bc549c0a7f34b37b1c777e5916b","source":{"kind":"arxiv","id":"1811.04346","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.04346","created_at":"2026-05-18T00:01:05Z"},{"alias_kind":"arxiv_version","alias_value":"1811.04346v1","created_at":"2026-05-18T00:01:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.04346","created_at":"2026-05-18T00:01:05Z"},{"alias_kind":"pith_short_12","alias_value":"PC2DYR4IL2C3","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"PC2DYR4IL2C3ZGKE","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"PC2DYR4I","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:PC2DYR4IL2C3ZGKET6IVP74EQB","target":"record","payload":{"canonical_record":{"source":{"id":"1811.04346","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-11-11T04:17:10Z","cross_cats_sorted":[],"title_canon_sha256":"e09ef08155e894e283ef280f06fd7062893fcbdfd3b6c2fe3b04825f2067d2a9","abstract_canon_sha256":"c122b9673ca0e98009c3a74a55ed93c79f7a50b9894d12a0090cb57df4a60054"},"schema_version":"1.0"},"canonical_sha256":"78b43c47885e85bc99449f9157ff848064d93bc549c0a7f34b37b1c777e5916b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:01:05.498120Z","signature_b64":"jyp8rCxL5oXHf5T4OFicV9OZUn4geUGszRgunVB0n1tWqoHgpL1SwZhPn1GAcL/2RvAph8GNwkQNueGd8axiCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78b43c47885e85bc99449f9157ff848064d93bc549c0a7f34b37b1c777e5916b","last_reissued_at":"2026-05-18T00:01:05.497498Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:01:05.497498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1811.04346","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":"2026-05-18T00:01:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5RXQbyWrnPc+v0KhRrOxrc33oK4fAh7iE80nFpCI8Z+GIDwd72na5BpiNNtrHJOaRru4iynKzUIpkhqipxkxCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T06:50:29.902589Z"},"content_sha256":"e55ff92cd92939b3ee240eed5ee19a72c3a396f11aaef3f05020249deb525d87","schema_version":"1.0","event_id":"sha256:e55ff92cd92939b3ee240eed5ee19a72c3a396f11aaef3f05020249deb525d87"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:PC2DYR4IL2C3ZGKET6IVP74EQB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Face Quality Assessment","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Vishal Agarwal","submitted_at":"2018-11-11T04:17:10Z","abstract_excerpt":"Face image quality is an important factor in facial recognition systems as its verification and recognition accuracy is highly dependent on the quality of image presented. Rejecting low quality images can significantly increase the accuracy of any facial recognition system. In this project, a simple approach is presented to train a deep convolutional neural network to perform end-to-end face image quality assessment. The work is done in 2 stages : First, generation of quality score label and secondly, training a deep convolutional neural network in a supervised manner to predict quality score "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.04346","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":"2026-05-18T00:01:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zmO11wrYmErGUH22Jn3Sct3fg0LDD/ICmO/Dbvig/BMuw8J39UPGeH8OyNozhqsH1Q/HBBKRDhg8EaEFKps8BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-09T06:50:29.903352Z"},"content_sha256":"72df9c06a130e8befa362b2758a4514dc53db62bf2cf464320ea03e9e20fb82e","schema_version":"1.0","event_id":"sha256:72df9c06a130e8befa362b2758a4514dc53db62bf2cf464320ea03e9e20fb82e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PC2DYR4IL2C3ZGKET6IVP74EQB/bundle.json","state_url":"https://pith.science/pith/PC2DYR4IL2C3ZGKET6IVP74EQB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PC2DYR4IL2C3ZGKET6IVP74EQB/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":"2026-06-09T06:50:29Z","links":{"resolver":"https://pith.science/pith/PC2DYR4IL2C3ZGKET6IVP74EQB","bundle":"https://pith.science/pith/PC2DYR4IL2C3ZGKET6IVP74EQB/bundle.json","state":"https://pith.science/pith/PC2DYR4IL2C3ZGKET6IVP74EQB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PC2DYR4IL2C3ZGKET6IVP74EQB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:PC2DYR4IL2C3ZGKET6IVP74EQB","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":"c122b9673ca0e98009c3a74a55ed93c79f7a50b9894d12a0090cb57df4a60054","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-11-11T04:17:10Z","title_canon_sha256":"e09ef08155e894e283ef280f06fd7062893fcbdfd3b6c2fe3b04825f2067d2a9"},"schema_version":"1.0","source":{"id":"1811.04346","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1811.04346","created_at":"2026-05-18T00:01:05Z"},{"alias_kind":"arxiv_version","alias_value":"1811.04346v1","created_at":"2026-05-18T00:01:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.04346","created_at":"2026-05-18T00:01:05Z"},{"alias_kind":"pith_short_12","alias_value":"PC2DYR4IL2C3","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"PC2DYR4IL2C3ZGKE","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"PC2DYR4I","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:72df9c06a130e8befa362b2758a4514dc53db62bf2cf464320ea03e9e20fb82e","target":"graph","created_at":"2026-05-18T00:01:05Z","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":"Face image quality is an important factor in facial recognition systems as its verification and recognition accuracy is highly dependent on the quality of image presented. Rejecting low quality images can significantly increase the accuracy of any facial recognition system. In this project, a simple approach is presented to train a deep convolutional neural network to perform end-to-end face image quality assessment. The work is done in 2 stages : First, generation of quality score label and secondly, training a deep convolutional neural network in a supervised manner to predict quality score ","authors_text":"Vishal Agarwal","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-11-11T04:17:10Z","title":"Deep Face Quality Assessment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.04346","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:e55ff92cd92939b3ee240eed5ee19a72c3a396f11aaef3f05020249deb525d87","target":"record","created_at":"2026-05-18T00:01:05Z","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":"c122b9673ca0e98009c3a74a55ed93c79f7a50b9894d12a0090cb57df4a60054","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-11-11T04:17:10Z","title_canon_sha256":"e09ef08155e894e283ef280f06fd7062893fcbdfd3b6c2fe3b04825f2067d2a9"},"schema_version":"1.0","source":{"id":"1811.04346","kind":"arxiv","version":1}},"canonical_sha256":"78b43c47885e85bc99449f9157ff848064d93bc549c0a7f34b37b1c777e5916b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"78b43c47885e85bc99449f9157ff848064d93bc549c0a7f34b37b1c777e5916b","first_computed_at":"2026-05-18T00:01:05.497498Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:01:05.497498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jyp8rCxL5oXHf5T4OFicV9OZUn4geUGszRgunVB0n1tWqoHgpL1SwZhPn1GAcL/2RvAph8GNwkQNueGd8axiCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:01:05.498120Z","signed_message":"canonical_sha256_bytes"},"source_id":"1811.04346","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e55ff92cd92939b3ee240eed5ee19a72c3a396f11aaef3f05020249deb525d87","sha256:72df9c06a130e8befa362b2758a4514dc53db62bf2cf464320ea03e9e20fb82e"],"state_sha256":"2cb9c6ae368c48258afcb06a03a918ee87341191dde0b07cd0a32a2b88be1a94"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vUZQDhwBNY+JlNBJk5HrVCbmFxczSZW1/9FERsYq5577RjQCg4m0o7cC3pIef6h6RKEPHAdZptlKvRWu0LrnCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-09T06:50:29.908308Z","bundle_sha256":"78b9fd6cb04b92d3aecaf5a8f1adcb92d27f67afeec4942ffc43a46946f53578"}}