TY - GEN
T1 - Emotion recognition from multi-modal information
AU - Wu, Chung Hsien
AU - Lin, Jen Chun
AU - Wei, Wen Li
AU - Cheng, Kuan Chun
PY - 2013
Y1 - 2013
N2 - Emotion recognition is the ability to detect what people are feeling from moment to moment and to understand the connection between their feelings and verbal/non-verbal expressions. When you are aware of your emotions, you can think clearly and creatively, manage stress and challenges, communicate well with others, and display trust, empathy, and confidence. In today's world, human-computer interaction (HCI) interface undoubtedly plays an important role in our daily life. Toward harmonious HCI interface, automated analysis of human emotion has attracted increasing attention from the researchers in multidisciplinary research fields. In this paper, we presents a survey on theoretical and practical work offering new and broad views of the latest research in emotion recognition from multi-modal information including facial and vocal expressions. A variety of theoretical background and applications ranging from salient emotional features, emotional-cognitive models, to multi-modal data fusion strategies is surveyed for emotion recognition on these modalities. Conclusions outline some of the existing emotion recognition challenges.
AB - Emotion recognition is the ability to detect what people are feeling from moment to moment and to understand the connection between their feelings and verbal/non-verbal expressions. When you are aware of your emotions, you can think clearly and creatively, manage stress and challenges, communicate well with others, and display trust, empathy, and confidence. In today's world, human-computer interaction (HCI) interface undoubtedly plays an important role in our daily life. Toward harmonious HCI interface, automated analysis of human emotion has attracted increasing attention from the researchers in multidisciplinary research fields. In this paper, we presents a survey on theoretical and practical work offering new and broad views of the latest research in emotion recognition from multi-modal information including facial and vocal expressions. A variety of theoretical background and applications ranging from salient emotional features, emotional-cognitive models, to multi-modal data fusion strategies is surveyed for emotion recognition on these modalities. Conclusions outline some of the existing emotion recognition challenges.
UR - https://www.scopus.com/pages/publications/84893284201
UR - https://www.scopus.com/pages/publications/84893284201#tab=citedBy
U2 - 10.1109/APSIPA.2013.6694347
DO - 10.1109/APSIPA.2013.6694347
M3 - Conference contribution
AN - SCOPUS:84893284201
SN - 9789869000604
T3 - 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
BT - 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
T2 - 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013
Y2 - 29 October 2013 through 1 November 2013
ER -