Rachael E. Jack;Vishal M. Patel;Pavan Turaga;Mayank Vatsa;Rama Chellappa;Alex Pentland;Richa Singh
{"title":"Best Paper Section IEEE International Conference on Automatic Face and Gesture Recognition 2021","authors":"Rachael E. Jack;Vishal M. Patel;Pavan Turaga;Mayank Vatsa;Rama Chellappa;Alex Pentland;Richa Singh","doi":"10.1109/TBIOM.2023.3296348","DOIUrl":null,"url":null,"abstract":"The IEEE International Conference on Automatic Face and Gesture Recognition (FG) is the premier international conference on vision-based automatic face and body behavior analysis and applications. Since the first meeting in Zurich in 1994, the FG conference has grown from a biennial conference to an annual meeting, presenting the advancements and latest research developments related to face and gesture analysis. FG2021 was planned to be an in-person meeting hosted in the historic city of Jodhpur, India. However, due to the COVID-19 pandemic situation, the organizing committee decided to hold FG2021 as an online conference from December 15 to 18, 2021. Over 142 papers were presented at FG2021 and based on the reviewers and area chair recommendations, PC Chairs invited a set of top reviewed papers as part of a special issue on “Best of Face & Gesture 2021” in the IEEE Transactions on Biometrics, Behavior, and Identity Science (T-BIOM). The meticulous review process of T-BIOM ensured that significantly extended research papers that were initially presented at FG2021 are included in this special issue. The nine accepted papers can be classified into three sets: (i) algorithms with 3D information based face/motion processing, (ii) algorithms towards head pose estimation, emotion recognition, differentiable rendering, dictionary attacks, and group detection, and (iii) the student engagement dataset for affect transfer learning for behavior prediction.","PeriodicalId":73307,"journal":{"name":"IEEE transactions on biometrics, behavior, and identity science","volume":"5 3","pages":"305-307"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8423754/10210132/10210211.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on biometrics, behavior, and identity science","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10210211/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The IEEE International Conference on Automatic Face and Gesture Recognition (FG) is the premier international conference on vision-based automatic face and body behavior analysis and applications. Since the first meeting in Zurich in 1994, the FG conference has grown from a biennial conference to an annual meeting, presenting the advancements and latest research developments related to face and gesture analysis. FG2021 was planned to be an in-person meeting hosted in the historic city of Jodhpur, India. However, due to the COVID-19 pandemic situation, the organizing committee decided to hold FG2021 as an online conference from December 15 to 18, 2021. Over 142 papers were presented at FG2021 and based on the reviewers and area chair recommendations, PC Chairs invited a set of top reviewed papers as part of a special issue on “Best of Face & Gesture 2021” in the IEEE Transactions on Biometrics, Behavior, and Identity Science (T-BIOM). The meticulous review process of T-BIOM ensured that significantly extended research papers that were initially presented at FG2021 are included in this special issue. The nine accepted papers can be classified into three sets: (i) algorithms with 3D information based face/motion processing, (ii) algorithms towards head pose estimation, emotion recognition, differentiable rendering, dictionary attacks, and group detection, and (iii) the student engagement dataset for affect transfer learning for behavior prediction.