{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:W37JSAGN7S7CB2QLB7VPPWDYDN","short_pith_number":"pith:W37JSAGN","schema_version":"1.0","canonical_sha256":"b6fe9900cdfcbe20ea0b0feaf7d8781b4e33b1f045f76b64e0231b4297feacb9","source":{"kind":"arxiv","id":"1902.01019","version":1},"attestation_state":"computed","paper":{"title":"Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"AmirAli Abdolrashidi, Shervin Minaee","submitted_at":"2019-02-04T03:15:13Z","abstract_excerpt":"Facial expression recognition has been an active research area over the past few decades, and it is still challenging due to the high intra-class variation.\n  Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG and LBP, followed by a classifier trained on a database of images or videos.\n  Most of these works perform reasonably well on datasets of images captured in a controlled condition, but fail to perform as good on more challenging datasets with more image variation and partial faces.\n  In recent years, several works proposed an end-to-end framework for "},"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":"1902.01019","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-04T03:15:13Z","cross_cats_sorted":[],"title_canon_sha256":"755ff6ebbfaf026f057eea5b8f2d5f924c1b9a2be9e9946c7475716f423aa4a4","abstract_canon_sha256":"bd2874f8bc5d0592a580155904ae080dd5533568c8bcaf1f01360fa2b3cce2ea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:49.889889Z","signature_b64":"0l7LPG2TGBtTIc5i9fvGtBm6wQKYWUXAEHoKw3iaGwqM60SIYbl99pvNsDR4LU5tPb314KVPEyUpOZLulBOkDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6fe9900cdfcbe20ea0b0feaf7d8781b4e33b1f045f76b64e0231b4297feacb9","last_reissued_at":"2026-05-17T23:54:49.889276Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:49.889276Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep-Emotion: Facial Expression Recognition Using Attentional Convolutional Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"AmirAli Abdolrashidi, Shervin Minaee","submitted_at":"2019-02-04T03:15:13Z","abstract_excerpt":"Facial expression recognition has been an active research area over the past few decades, and it is still challenging due to the high intra-class variation.\n  Traditional approaches for this problem rely on hand-crafted features such as SIFT, HOG and LBP, followed by a classifier trained on a database of images or videos.\n  Most of these works perform reasonably well on datasets of images captured in a controlled condition, but fail to perform as good on more challenging datasets with more image variation and partial faces.\n  In recent years, several works proposed an end-to-end framework for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.01019","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":"1902.01019","created_at":"2026-05-17T23:54:49.889395+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.01019v1","created_at":"2026-05-17T23:54:49.889395+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.01019","created_at":"2026-05-17T23:54:49.889395+00:00"},{"alias_kind":"pith_short_12","alias_value":"W37JSAGN7S7C","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_16","alias_value":"W37JSAGN7S7CB2QL","created_at":"2026-05-18T12:33:30.264802+00:00"},{"alias_kind":"pith_short_8","alias_value":"W37JSAGN","created_at":"2026-05-18T12:33:30.264802+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"1907.09380","citing_title":"DeepIris: Iris Recognition Using A Deep Learning Approach","ref_index":17,"is_internal_anchor":true},{"citing_arxiv_id":"1907.10838","citing_title":"A Fine-Grained Facial Expression Database for End-to-End Multi-Pose Facial Expression Recognition","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W37JSAGN7S7CB2QLB7VPPWDYDN","json":"https://pith.science/pith/W37JSAGN7S7CB2QLB7VPPWDYDN.json","graph_json":"https://pith.science/api/pith-number/W37JSAGN7S7CB2QLB7VPPWDYDN/graph.json","events_json":"https://pith.science/api/pith-number/W37JSAGN7S7CB2QLB7VPPWDYDN/events.json","paper":"https://pith.science/paper/W37JSAGN"},"agent_actions":{"view_html":"https://pith.science/pith/W37JSAGN7S7CB2QLB7VPPWDYDN","download_json":"https://pith.science/pith/W37JSAGN7S7CB2QLB7VPPWDYDN.json","view_paper":"https://pith.science/paper/W37JSAGN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.01019&json=true","fetch_graph":"https://pith.science/api/pith-number/W37JSAGN7S7CB2QLB7VPPWDYDN/graph.json","fetch_events":"https://pith.science/api/pith-number/W37JSAGN7S7CB2QLB7VPPWDYDN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W37JSAGN7S7CB2QLB7VPPWDYDN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W37JSAGN7S7CB2QLB7VPPWDYDN/action/storage_attestation","attest_author":"https://pith.science/pith/W37JSAGN7S7CB2QLB7VPPWDYDN/action/author_attestation","sign_citation":"https://pith.science/pith/W37JSAGN7S7CB2QLB7VPPWDYDN/action/citation_signature","submit_replication":"https://pith.science/pith/W37JSAGN7S7CB2QLB7VPPWDYDN/action/replication_record"}},"created_at":"2026-05-17T23:54:49.889395+00:00","updated_at":"2026-05-17T23:54:49.889395+00:00"}