{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:ZKQCGFESUT7WBLBYFKMTGOK5KD","short_pith_number":"pith:ZKQCGFES","canonical_record":{"source":{"id":"1602.03256","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-02-10T03:52:11Z","cross_cats_sorted":[],"title_canon_sha256":"a6215067fbafb2236f250ddf76863859bec7036a9eab855af69c477a470d9318","abstract_canon_sha256":"bdca576fd0de53d39cafb68d15721e36ccc154255f065ead461f221889915d97"},"schema_version":"1.0"},"canonical_sha256":"caa0231492a4ff60ac382a9933395d50fe7601f5af976e0fa4f3704baba4d28b","source":{"kind":"arxiv","id":"1602.03256","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.03256","created_at":"2026-05-18T01:21:00Z"},{"alias_kind":"arxiv_version","alias_value":"1602.03256v1","created_at":"2026-05-18T01:21:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.03256","created_at":"2026-05-18T01:21:00Z"},{"alias_kind":"pith_short_12","alias_value":"ZKQCGFESUT7W","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"ZKQCGFESUT7WBLBY","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"ZKQCGFES","created_at":"2026-05-18T12:30:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:ZKQCGFESUT7WBLBYFKMTGOK5KD","target":"record","payload":{"canonical_record":{"source":{"id":"1602.03256","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-02-10T03:52:11Z","cross_cats_sorted":[],"title_canon_sha256":"a6215067fbafb2236f250ddf76863859bec7036a9eab855af69c477a470d9318","abstract_canon_sha256":"bdca576fd0de53d39cafb68d15721e36ccc154255f065ead461f221889915d97"},"schema_version":"1.0"},"canonical_sha256":"caa0231492a4ff60ac382a9933395d50fe7601f5af976e0fa4f3704baba4d28b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:21:00.401825Z","signature_b64":"cDRds4Ij5insSzUWhSVrVvIRSgeEeDG4UVr4PZ1pNsFg54W9tSIttkk/f4D+deWZ+6DTgrQA+KoZMxoR7d2/Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"caa0231492a4ff60ac382a9933395d50fe7601f5af976e0fa4f3704baba4d28b","last_reissued_at":"2026-05-18T01:21:00.401295Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:21:00.401295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1602.03256","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":"2026-05-18T01:21:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"H1QvRyz2CtbnNr6/ia9EtL3rPk51CQSZwKcDT3Fs4V/wRGgOgfQxzsev4kuWUR6GxEUbXdsYmW9626YWy6OiCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T02:37:36.976310Z"},"content_sha256":"865507bc208fa2b5104267991d2057510ea7afe860d58b6c92e0208366ced950","schema_version":"1.0","event_id":"sha256:865507bc208fa2b5104267991d2057510ea7afe860d58b6c92e0208366ced950"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:ZKQCGFESUT7WBLBYFKMTGOK5KD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improved Eigenfeature Regularization for Face Identification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bappaditya Mandal","submitted_at":"2016-02-10T03:52:11Z","abstract_excerpt":"In this work, we propose to divide each class (a person) into subclasses using spatial partition trees which helps in better capturing the intra-personal variances arising from the appearances of the same individual. We perform a comprehensive analysis on within-class and within-subclass eigenspectrums of face images and propose a novel method of eigenspectrum modeling which extracts discriminative features of faces from both within-subclass and total or between-subclass scatter matrices. Effective low-dimensional face discriminative features are extracted for face recognition (FR) after perfo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.03256","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":"2026-05-18T01:21:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jj7pDga377E7/5DHDCTcohzWuaf4PXOTS3J8Io0xQ1fVLaoJcabSyX8QGAPN1x1A9KQXa/m/5tszKl8am9d6Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T02:37:36.976670Z"},"content_sha256":"11eed4ef760e749fd827cd49ab0fd9085820834358fb8c68c74b249bf0bcf3ad","schema_version":"1.0","event_id":"sha256:11eed4ef760e749fd827cd49ab0fd9085820834358fb8c68c74b249bf0bcf3ad"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZKQCGFESUT7WBLBYFKMTGOK5KD/bundle.json","state_url":"https://pith.science/pith/ZKQCGFESUT7WBLBYFKMTGOK5KD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZKQCGFESUT7WBLBYFKMTGOK5KD/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":"2026-06-03T02:37:36Z","links":{"resolver":"https://pith.science/pith/ZKQCGFESUT7WBLBYFKMTGOK5KD","bundle":"https://pith.science/pith/ZKQCGFESUT7WBLBYFKMTGOK5KD/bundle.json","state":"https://pith.science/pith/ZKQCGFESUT7WBLBYFKMTGOK5KD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZKQCGFESUT7WBLBYFKMTGOK5KD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:ZKQCGFESUT7WBLBYFKMTGOK5KD","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":"bdca576fd0de53d39cafb68d15721e36ccc154255f065ead461f221889915d97","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-02-10T03:52:11Z","title_canon_sha256":"a6215067fbafb2236f250ddf76863859bec7036a9eab855af69c477a470d9318"},"schema_version":"1.0","source":{"id":"1602.03256","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1602.03256","created_at":"2026-05-18T01:21:00Z"},{"alias_kind":"arxiv_version","alias_value":"1602.03256v1","created_at":"2026-05-18T01:21:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.03256","created_at":"2026-05-18T01:21:00Z"},{"alias_kind":"pith_short_12","alias_value":"ZKQCGFESUT7W","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_16","alias_value":"ZKQCGFESUT7WBLBY","created_at":"2026-05-18T12:30:53Z"},{"alias_kind":"pith_short_8","alias_value":"ZKQCGFES","created_at":"2026-05-18T12:30:53Z"}],"graph_snapshots":[{"event_id":"sha256:11eed4ef760e749fd827cd49ab0fd9085820834358fb8c68c74b249bf0bcf3ad","target":"graph","created_at":"2026-05-18T01:21:00Z","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":"In this work, we propose to divide each class (a person) into subclasses using spatial partition trees which helps in better capturing the intra-personal variances arising from the appearances of the same individual. We perform a comprehensive analysis on within-class and within-subclass eigenspectrums of face images and propose a novel method of eigenspectrum modeling which extracts discriminative features of faces from both within-subclass and total or between-subclass scatter matrices. Effective low-dimensional face discriminative features are extracted for face recognition (FR) after perfo","authors_text":"Bappaditya Mandal","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-02-10T03:52:11Z","title":"Improved Eigenfeature Regularization for Face Identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.03256","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":"sha256:865507bc208fa2b5104267991d2057510ea7afe860d58b6c92e0208366ced950","target":"record","created_at":"2026-05-18T01:21:00Z","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":"bdca576fd0de53d39cafb68d15721e36ccc154255f065ead461f221889915d97","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-02-10T03:52:11Z","title_canon_sha256":"a6215067fbafb2236f250ddf76863859bec7036a9eab855af69c477a470d9318"},"schema_version":"1.0","source":{"id":"1602.03256","kind":"arxiv","version":1}},"canonical_sha256":"caa0231492a4ff60ac382a9933395d50fe7601f5af976e0fa4f3704baba4d28b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"caa0231492a4ff60ac382a9933395d50fe7601f5af976e0fa4f3704baba4d28b","first_computed_at":"2026-05-18T01:21:00.401295Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:21:00.401295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"cDRds4Ij5insSzUWhSVrVvIRSgeEeDG4UVr4PZ1pNsFg54W9tSIttkk/f4D+deWZ+6DTgrQA+KoZMxoR7d2/Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:21:00.401825Z","signed_message":"canonical_sha256_bytes"},"source_id":"1602.03256","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:865507bc208fa2b5104267991d2057510ea7afe860d58b6c92e0208366ced950","sha256:11eed4ef760e749fd827cd49ab0fd9085820834358fb8c68c74b249bf0bcf3ad"],"state_sha256":"80668ddacf4d7034c371b0a8f70bd93bda959773ff9de15495d290893f071dcc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uDbJKyEAJkZFGeqWnsy5ogHUz9fZS9rljrqh3fH9drzZl0xksRgV/GeP3yPGr6Q/Kr1zJSK2guFukFr1/xFXBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T02:37:36.978600Z","bundle_sha256":"87937799df76fdacdf99646177bfef846110d725928b4595b449733b6f46c09c"}}