アクセス数 : ?
ダウンロード数 : ?
ID 116447
著者
Orgil, Jargalsaikhan Tokushima University
Shagdar, Ganbold Mongolian University of Science Technology
キーワード
deep emotion recognition
emotion recognition
emotion
body language
intonation
資料タイプ
学術雑誌論文
抄録
Automatic understanding of human emotion in a wild setting using audiovisual signals is extremely challenging. Latent continuous dimensions can be used to accomplish the analysis of human emotional states, behaviors, and reactions displayed in real-world settings. Moreover, Valence and Arousal combinations constitute well-known and effective representations of emotions. In this paper, a new Non-inertial loss function is proposed to train emotion recognition deep learning models. It is evaluated in wild settings using four types of candidate networks with different pipelines and sequence lengths. It is then compared to the Concordance Correlation Coefficient (CCC) and Mean Squared Error (MSE) losses commonly used for training. To prove its effectiveness on efficiency and stability in continuous or non-continuous input data, experiments were performed using the Aff-Wild dataset. Encouraging results were obtained.
掲載誌名
Journal of Signal Processing
ISSN
18801013
出版者
Research Institute of Signal Processing
25
2
開始ページ
73
終了ページ
85
発行日
2021-03-01
備考
利用は著作権の範囲内に限られる。
EDB ID
出版社版DOI
出版社版URL
フルテキストファイル
言語
eng
著者版フラグ
出版社版
部局
理工学系