{"doi":"10.1109/taffc.2014.2316151","title":"Vision and Attention Theory Based Sampling for Continuous Facial Emotion Recognition","abstract":null,"journal":"IEEE Transactions on Affective Computing","year":2014,"id":679441,"datarank":0.5983476069846413,"base_score":3.9889840465642745,"endowment":3.9889840465642745,"self_citation_contribution":0.5983476069846413,"citation_network_contribution":0.0,"self_endowment_contribution":0.5983476069846413,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":53,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":799101,"name":"Bir Bhanu","orcid":"0000-0001-8971-6416","position":1,"is_corresponding":false},{"id":1775221,"name":"Ninad S. Thakoor","orcid":null,"position":2,"is_corresponding":false},{"id":1775220,"name":"Albert C. Cruz","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Vision and Attention Theory Based Sampling for Continuous Facial Emotion Recognition","abstract":"Affective computing-the emergent field in which computers detect emotions and project appropriate expressions of their own-has reached a bottleneck where algorithms are not able to infer a person's emotions from natural and spontaneous facial expressions captured in video. While the field of emotion recognition has seen many advances in the past decade, a facial emotion recognition approach has not yet been revealed which performs well in unconstrained settings. In this paper, we propose a principled method which addresses the temporal dynamics of facial emotions and expressions in video with a sampling approach inspired from human perceptual psychology. We test the efficacy of the method on the Audio/Visual Emotion Challenge 2011 and 2012, CohnKanade and the MMI Facial Expression Database. The method shows an average improvement of 9.8 percent over the baseline for weighted accuracy on the Audio/Visual Emotion Challenge 2011 video-based frame-level subchallenge testing set.","is_dataset_classified":null,"base_score":3.9889840465642745,"endowment":3.9889840465642745,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"26207759","pmcid":null,"openalex_id":"https://openalex.org/W2022024883","authors":[],"funders":[{"funder_name":"US National Science Foundation (NSF)","grant_id":"0727129","title":null},{"funder_name":"US National Science Foundation (NSF)","grant_id":"0905671","title":null},{"funder_name":"NSF IGERT: Video Bioinformatics","grant_id":"DGE 0903667","title":null}],"total_grants":3,"fwci":5.1346,"citation_percentile":0.94912081,"influential_citations":0,"citation_trend":[{"year":2014,"count":2},{"year":2015,"count":3},{"year":2016,"count":4},{"year":2017,"count":8},{"year":2018,"count":7},{"year":2019,"count":6},{"year":2020,"count":6},{"year":2021,"count":8},{"year":2022,"count":5},{"year":2023,"count":2},{"year":2024,"count":1},{"year":2026,"count":1}],"oa_status":"green","license":"https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html","oa_locations":[{"url":"https://escholarship.org/content/qt0b05b94f/qt0b05b94f.pdf?t=q606ub","host_type":"repository"},{"url":"https://escholarship.org/content/qt0b05b94f/qt0b05b94f.pdf?t=q606ub","host_type":"repository"},{"url":"http://xplorestaging.ieee.org/ielx7/5165369/6965689/06784434.pdf?arnumber=6784434","host_type":"publisher"},{"url":"https://escholarship.org/uc/item/0b05b94f","host_type":"repository"},{"url":"https://doi.org/10.1109/taffc.2014.2316151","host_type":"journal"}],"fields_of_study":["Emotion and Mood Recognition","Face and Expression Recognition","Gaze Tracking and Assistive Technology"],"mesh_terms":[],"keywords":["Facial expression","Emotion recognition","Emotion perception","Computer science","Bottleneck","Set (abstract data type)","Perception","Affective computing","Field (mathematics)","Artificial intelligence","Speech recognition","Frame (networking)","Dynamics (music)","Emotion detection","Psychology","Mathematics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-17T13:11:01.281031Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}