TY - GEN
T1 - Mix emotion recognition from facial expression using SVM-CRF sequence classifier
AU - Liliana, Dewi Yanti
AU - Basaruddin, Chan
AU - Widyanto, M. Rahmat
N1 - Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/8/10
Y1 - 2017/8/10
N2 - Recently, emotion recognition has gained increasing attention in various applications related to Social Signal Processing (SSP) and human affect. The existing research is mainly focused on six basic emotions (happy, sad, fear, disgust, angry, and surprise). However human expresses many kind of emotions, including mix emotion which has not been explored due to its complexity. We model 12 types of mix emotion recognition from facial expression in a sequence of images using two-stages learning which combines Support Vector Machines (SVM) and Conditional Random Fields (CRF) as sequence classifiers. SVM classifies each image frame and produce emotion label output, subsequently it becomes the input for CRF which yields the mix emotion label of the corresponding observation sequence. We evaluate our proposed model on modified image frames of Cohn Kanade+ dataset, and on our own made mix emotion dataset. We also compare our model with the original CRF model, and our model shows a superior performance result.
AB - Recently, emotion recognition has gained increasing attention in various applications related to Social Signal Processing (SSP) and human affect. The existing research is mainly focused on six basic emotions (happy, sad, fear, disgust, angry, and surprise). However human expresses many kind of emotions, including mix emotion which has not been explored due to its complexity. We model 12 types of mix emotion recognition from facial expression in a sequence of images using two-stages learning which combines Support Vector Machines (SVM) and Conditional Random Fields (CRF) as sequence classifiers. SVM classifies each image frame and produce emotion label output, subsequently it becomes the input for CRF which yields the mix emotion label of the corresponding observation sequence. We evaluate our proposed model on modified image frames of Cohn Kanade+ dataset, and on our own made mix emotion dataset. We also compare our model with the original CRF model, and our model shows a superior performance result.
KW - Facial expression
KW - Mix emotion recognition
KW - SVM-CRF classifier
KW - Sequence classifier
UR - http://www.scopus.com/inward/record.url?scp=85039070454&partnerID=8YFLogxK
U2 - 10.1145/3127942.3127958
DO - 10.1145/3127942.3127958
M3 - Conference contribution
AN - SCOPUS:85039070454
T3 - ACM International Conference Proceeding Series
SP - 27
EP - 31
BT - Proceedings of 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
PB - Association for Computing Machinery
T2 - 2017 International Conference on Algorithms, Computing and Systems, ICACS 2017
Y2 - 10 August 2017 through 13 August 2017
ER -