TY - GEN
T1 - Video-based emotion identification using face alignment and support vector machines
AU - Jang, Gil Jin
AU - Jo, Ahra
AU - Park, Jeong Sik
N1 - Publisher Copyright:
Copyright © 2014 ACM.
PY - 2014/10/29
Y1 - 2014/10/29
N2 - This abstract introduces an efficient method for identifying various facial expressions from image inputs. To recognize the emotions of the facial expressions, a number of facial feature points were extracted. The extracted feature points are then transformed to 49-dimensional feature vectors which are robust to scale and translational variations, and the facial emotions are recognized by a support vector machine (SVM). Based on the experimental results, SVM performance was obtained by 50.8% for 6 emotion classification, and 78.0% for 3 emotions.
AB - This abstract introduces an efficient method for identifying various facial expressions from image inputs. To recognize the emotions of the facial expressions, a number of facial feature points were extracted. The extracted feature points are then transformed to 49-dimensional feature vectors which are robust to scale and translational variations, and the facial emotions are recognized by a support vector machine (SVM). Based on the experimental results, SVM performance was obtained by 50.8% for 6 emotion classification, and 78.0% for 3 emotions.
KW - Active Shape Model
KW - Emotion Recognition
KW - Support Vector Machine.
UR - http://www.scopus.com/inward/record.url?scp=84914698776&partnerID=8YFLogxK
U2 - 10.1145/2658861.2658943
DO - 10.1145/2658861.2658943
M3 - Conference contribution
AN - SCOPUS:84914698776
T3 - HAI 2014 - Proceedings of the 2nd International Conference on Human-Agent Interaction
SP - 285
EP - 286
BT - HAI 2014 - Proceedings of the 2nd International Conference on Human-Agent Interaction
PB - Association for Computing Machinery
T2 - 2nd International Conference on Human-Agent Interaction, HAI 2014
Y2 - 29 October 2014 through 31 October 2014
ER -