TY - GEN
T1 - Normalizing multi-subject variation for drivers' emotion recognition
AU - Jinjun, Wang
AU - Yihong, Gong
PY - 2009
Y1 - 2009
N2 - The paper attempts the recognition of multiple drivers' emotional state from physiological signals. The major challenge of the research is the severe inter-subject variation such that it is extreme difficult to build a general model for multiple drivers. In this paper, we focus on discovering an optimal feature mapping by utilizing the additional attribute from the drivers. Two models are reported, specifically an auxiliary dimension model and a factorization model. Experimental results show that the proposed method outperform existing algorithms used for emotional state recognition.
AB - The paper attempts the recognition of multiple drivers' emotional state from physiological signals. The major challenge of the research is the severe inter-subject variation such that it is extreme difficult to build a general model for multiple drivers. In this paper, we focus on discovering an optimal feature mapping by utilizing the additional attribute from the drivers. Two models are reported, specifically an auxiliary dimension model and a factorization model. Experimental results show that the proposed method outperform existing algorithms used for emotional state recognition.
UR - https://www.scopus.com/pages/publications/70449579358
U2 - 10.1109/ICME.2009.5202507
DO - 10.1109/ICME.2009.5202507
M3 - 会议稿件
AN - SCOPUS:70449579358
SN - 9781424442911
T3 - Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
SP - 354
EP - 357
BT - Proceedings - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
T2 - 2009 IEEE International Conference on Multimedia and Expo, ICME 2009
Y2 - 28 June 2009 through 3 July 2009
ER -