Normalizing multi-subject variation for drivers' emotion recognition
ICME（2009）
摘要
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.
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关键词
emotional state recognition,optimisation,major challenge,factorization model,multi-subject variation,auxiliary dimension model,general model,emotional state,emotion recognition,feature extraction,additional attribute,driver information systems,multisubject variation normalization,optimal feature mapping,driver emotional state recognition,multiple driver,physiological signal,accuracy,frequency modulation,factor model,sensors,support vector machines
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