{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:6XQFMTFTXDAYOKPT2X6X745WW7","short_pith_number":"pith:6XQFMTFT","canonical_record":{"source":{"id":"1702.04280","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-02-14T16:34:05Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"43b429b7cd92c4e4dbacc359200c392175358cad5c37e4495c60f9d357cf4919","abstract_canon_sha256":"c02feb4689708d9429fa20b7070157f935a690678eb7490e575d9f4ecef9789d"},"schema_version":"1.0"},"canonical_sha256":"f5e0564cb3b8c18729f3d5fd7ff3b6b7cc5fd300df7c874dcbfe2d900912bf69","source":{"kind":"arxiv","id":"1702.04280","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.04280","created_at":"2026-05-18T00:49:31Z"},{"alias_kind":"arxiv_version","alias_value":"1702.04280v2","created_at":"2026-05-18T00:49:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.04280","created_at":"2026-05-18T00:49:31Z"},{"alias_kind":"pith_short_12","alias_value":"6XQFMTFTXDAY","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6XQFMTFTXDAYOKPT","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6XQFMTFT","created_at":"2026-05-18T12:31:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:6XQFMTFTXDAYOKPT2X6X745WW7","target":"record","payload":{"canonical_record":{"source":{"id":"1702.04280","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-02-14T16:34:05Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"43b429b7cd92c4e4dbacc359200c392175358cad5c37e4495c60f9d357cf4919","abstract_canon_sha256":"c02feb4689708d9429fa20b7070157f935a690678eb7490e575d9f4ecef9789d"},"schema_version":"1.0"},"canonical_sha256":"f5e0564cb3b8c18729f3d5fd7ff3b6b7cc5fd300df7c874dcbfe2d900912bf69","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:31.419892Z","signature_b64":"bRi7gxzMsx8oWvQmjhtcnTrH30+sWeYFXKdV9yUpXvW6x2pNnXoh1UrdOvUcF1sId8uUHPbTGwVXEXS5blX0DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f5e0564cb3b8c18729f3d5fd7ff3b6b7cc5fd300df7c874dcbfe2d900912bf69","last_reissued_at":"2026-05-18T00:49:31.419331Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:31.419331Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1702.04280","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:49:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HRAQJF1ArDDvIFyVISVIsQK5VdCNWs7Y8zEDiLBZaTeg6eNH2Nj/ug5bTBmDNmxuPBUgd2ltkB5DDFDycDNFBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T15:48:41.334797Z"},"content_sha256":"8f3b6d75f2e15b1b9f8a60d2aa966baa6533b3c8e02ee6efdcae3d3f9fdf999a","schema_version":"1.0","event_id":"sha256:8f3b6d75f2e15b1b9f8a60d2aa966baa6533b3c8e02ee6efdcae3d3f9fdf999a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:6XQFMTFTXDAYOKPT2X6X745WW7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Afshin Dehghan, Enrique G. Ortiz, Guang Shu, Syed Zain Masood","submitted_at":"2017-02-14T16:34:05Z","abstract_excerpt":"This paper describes the details of Sighthound's fully automated age, gender and emotion recognition system. The backbone of our system consists of several deep convolutional neural networks that are not only computationally inexpensive, but also provide state-of-the-art results on several competitive benchmarks. To power our novel deep networks, we collected large labeled datasets through a semi-supervised pipeline to reduce the annotation effort/time. We tested our system on several public benchmarks and report outstanding results. Our age, gender and emotion recognition models are available"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.04280","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":""},"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":"2026-05-18T00:49:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v2sAKflTvQgI1FExxtavcBYgbwsMK3XvJB/HNl6/FRIehRNHQwbvuxeV1u5fvtR47m2M1AR8jpIzAjE2EXniDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T15:48:41.335154Z"},"content_sha256":"f2c14ccaa7c8208779d84e3b4df7ec2a50c17bc60e73749e41ffe50ca91f8b53","schema_version":"1.0","event_id":"sha256:f2c14ccaa7c8208779d84e3b4df7ec2a50c17bc60e73749e41ffe50ca91f8b53"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6XQFMTFTXDAYOKPT2X6X745WW7/bundle.json","state_url":"https://pith.science/pith/6XQFMTFTXDAYOKPT2X6X745WW7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6XQFMTFTXDAYOKPT2X6X745WW7/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":"2026-05-20T15:48:41Z","links":{"resolver":"https://pith.science/pith/6XQFMTFTXDAYOKPT2X6X745WW7","bundle":"https://pith.science/pith/6XQFMTFTXDAYOKPT2X6X745WW7/bundle.json","state":"https://pith.science/pith/6XQFMTFTXDAYOKPT2X6X745WW7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6XQFMTFTXDAYOKPT2X6X745WW7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:6XQFMTFTXDAYOKPT2X6X745WW7","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":"c02feb4689708d9429fa20b7070157f935a690678eb7490e575d9f4ecef9789d","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-02-14T16:34:05Z","title_canon_sha256":"43b429b7cd92c4e4dbacc359200c392175358cad5c37e4495c60f9d357cf4919"},"schema_version":"1.0","source":{"id":"1702.04280","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.04280","created_at":"2026-05-18T00:49:31Z"},{"alias_kind":"arxiv_version","alias_value":"1702.04280v2","created_at":"2026-05-18T00:49:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.04280","created_at":"2026-05-18T00:49:31Z"},{"alias_kind":"pith_short_12","alias_value":"6XQFMTFTXDAY","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_16","alias_value":"6XQFMTFTXDAYOKPT","created_at":"2026-05-18T12:31:03Z"},{"alias_kind":"pith_short_8","alias_value":"6XQFMTFT","created_at":"2026-05-18T12:31:03Z"}],"graph_snapshots":[{"event_id":"sha256:f2c14ccaa7c8208779d84e3b4df7ec2a50c17bc60e73749e41ffe50ca91f8b53","target":"graph","created_at":"2026-05-18T00:49:31Z","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":"This paper describes the details of Sighthound's fully automated age, gender and emotion recognition system. The backbone of our system consists of several deep convolutional neural networks that are not only computationally inexpensive, but also provide state-of-the-art results on several competitive benchmarks. To power our novel deep networks, we collected large labeled datasets through a semi-supervised pipeline to reduce the annotation effort/time. We tested our system on several public benchmarks and report outstanding results. Our age, gender and emotion recognition models are available","authors_text":"Afshin Dehghan, Enrique G. Ortiz, Guang Shu, Syed Zain Masood","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-02-14T16:34:05Z","title":"DAGER: Deep Age, Gender and Emotion Recognition Using Convolutional Neural Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.04280","kind":"arxiv","version":2},"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":"sha256:8f3b6d75f2e15b1b9f8a60d2aa966baa6533b3c8e02ee6efdcae3d3f9fdf999a","target":"record","created_at":"2026-05-18T00:49:31Z","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":"c02feb4689708d9429fa20b7070157f935a690678eb7490e575d9f4ecef9789d","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-02-14T16:34:05Z","title_canon_sha256":"43b429b7cd92c4e4dbacc359200c392175358cad5c37e4495c60f9d357cf4919"},"schema_version":"1.0","source":{"id":"1702.04280","kind":"arxiv","version":2}},"canonical_sha256":"f5e0564cb3b8c18729f3d5fd7ff3b6b7cc5fd300df7c874dcbfe2d900912bf69","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f5e0564cb3b8c18729f3d5fd7ff3b6b7cc5fd300df7c874dcbfe2d900912bf69","first_computed_at":"2026-05-18T00:49:31.419331Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:49:31.419331Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bRi7gxzMsx8oWvQmjhtcnTrH30+sWeYFXKdV9yUpXvW6x2pNnXoh1UrdOvUcF1sId8uUHPbTGwVXEXS5blX0DA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:49:31.419892Z","signed_message":"canonical_sha256_bytes"},"source_id":"1702.04280","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8f3b6d75f2e15b1b9f8a60d2aa966baa6533b3c8e02ee6efdcae3d3f9fdf999a","sha256:f2c14ccaa7c8208779d84e3b4df7ec2a50c17bc60e73749e41ffe50ca91f8b53"],"state_sha256":"d912901a972119ea404f43101b07f0dfe110b3f9a84de759919ff29ca474492f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mF1s8FHiA/WYMdJMe2oLk1wIEMAxy4LA4lNOcnPc+FGHtVOLLNcKq9L6gP51wfrQk7K1h/uszBHag65Xgn77CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T15:48:41.337070Z","bundle_sha256":"30bed915b3a2925bc6d926b72fb9e0cd21ffb9a6a6a1f556ddec855e71065967"}}