TY - GEN
T1 - Classification of Facial Expression In-the-Wild based on Ensemble of Multi-head Cross Attention Networks
AU - Jeong, Jae Yeop
AU - Hong, Yeong Gi
AU - Kim, Daun
AU - Jeong, Jin Woo
AU - Jung, Yuchul
AU - Kim, Sang Ho
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - How to build a system for robust classification and recognition of facial expressions has been one of the most important research issues for successful interactive computing applications. However, previous datasets and studies mainly focused on facial expression recognition in a controlled/lab setting, therefore, could hardly be generalized in a more practical and real-life environment. The Affective Behavior Analysis in-the-wild (ABAW) 2022 competition released a dataset consisting of various video clips of facial expressions in-the-wild. In this paper, we propose a method based on the ensemble of multi-head cross attention networks to address the facial expression classification task introduced in the ABAW 2022 competition. We built a uni-task approach for this task, achieving the average F1-score of 34.60 on the validation set and 33.77 on the test set, ranking second place on the final leaderboard.
AB - How to build a system for robust classification and recognition of facial expressions has been one of the most important research issues for successful interactive computing applications. However, previous datasets and studies mainly focused on facial expression recognition in a controlled/lab setting, therefore, could hardly be generalized in a more practical and real-life environment. The Affective Behavior Analysis in-the-wild (ABAW) 2022 competition released a dataset consisting of various video clips of facial expressions in-the-wild. In this paper, we propose a method based on the ensemble of multi-head cross attention networks to address the facial expression classification task introduced in the ABAW 2022 competition. We built a uni-task approach for this task, achieving the average F1-score of 34.60 on the validation set and 33.77 on the test set, ranking second place on the final leaderboard.
UR - http://www.scopus.com/inward/record.url?scp=85137839822&partnerID=8YFLogxK
U2 - 10.1109/CVPRW56347.2022.00262
DO - 10.1109/CVPRW56347.2022.00262
M3 - Conference contribution
AN - SCOPUS:85137839822
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 2352
EP - 2357
BT - Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
PB - IEEE Computer Society
T2 - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022
Y2 - 19 June 2022 through 20 June 2022
ER -