TY - GEN
T1 - Demo
T2 - 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2015
AU - Chen, Min
AU - Hao, Yixue
AU - Li, Yong
AU - Wu, Di
AU - Huang, Dijiang
N1 - Publisher Copyright:
Copyright © is held by the author/owner(s).
PY - 2015/6/22
Y1 - 2015/6/22
N2 - In order to improve the accuracy and efficiency of emotion recognition, we design a novel system called Learning through Interactive Video and Emotion-aware System (LIVES). LIVES includes data collection, emotion recognition, and result validation, as well as emotion feedback. We adopt transfer learning to label and validate moods in LIVES, while the emotion can be classified into six types of mood in a reasonable accuracy. Through transfer learning, the time-consuming and labor-intensive processing cost on data collection and labeling can also be greatly reduced. In our prototype system, LIVES is used to enhance an emotion-aware robot’s intelligence provided by cloud. LIVES-based emotion recognition is executed in the remote cloud while corresponding result is sent to the robot for emotion feedback. The experimental results demonstrate LIVES significantly improves the accuracy and effectiveness of emotion classification.
AB - In order to improve the accuracy and efficiency of emotion recognition, we design a novel system called Learning through Interactive Video and Emotion-aware System (LIVES). LIVES includes data collection, emotion recognition, and result validation, as well as emotion feedback. We adopt transfer learning to label and validate moods in LIVES, while the emotion can be classified into six types of mood in a reasonable accuracy. Through transfer learning, the time-consuming and labor-intensive processing cost on data collection and labeling can also be greatly reduced. In our prototype system, LIVES is used to enhance an emotion-aware robot’s intelligence provided by cloud. LIVES-based emotion recognition is executed in the remote cloud while corresponding result is sent to the robot for emotion feedback. The experimental results demonstrate LIVES significantly improves the accuracy and effectiveness of emotion classification.
KW - Affective interaction
KW - Sentiment anlysis
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=84966527286&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84966527286&partnerID=8YFLogxK
U2 - 10.1145/2746285.2764928
DO - 10.1145/2746285.2764928
M3 - Conference contribution
AN - SCOPUS:84966527286
T3 - Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)
SP - 399
EP - 400
BT - MobiHoc'15 - Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing
PB - Association for Computing Machinery
Y2 - 22 June 2015 through 25 June 2015
ER -