NDLI: Visual emotion recognition based on dynamic models

Content Provider	IEEE Xplore Digital Library
Author	Guoyun Lv Shuixian Hu Yangyu Fan Min Qi
Copyright Year	2013
Description	Author affiliation: Sch. of Electron. & Inf., Northwestern Polytech. Univ., Xian, China (Guoyun Lv; Shuixian Hu; Yangyu Fan; Min Qi)
Abstract	This paper introduces the semi-continuous Hidden Markov Model (HMM) and proposes a novel Dynamic Bayesian Network (DBN) model for dynamic visual emotion recognition. The former reduces the training complexity caused by mixture Gaussians by sharing the Condition Probability Densities (CPDs) of Gaussians among the states, and the latter adds a sub-state layer between state and observation layer based on traditional DBN framework and describes the dynamic process of visual emotion in detail. Experiments results show that semi-continuous HMM and three-layer DBN have better performance, and average emotion recognition rate of the semi-continuous HMM is 1.85% and 3.82% higher than those of classical HMM and mixture Gaussian HMM respectively, and average emotion recognition rate of three-layer DBN is 1.93% higher than that of traditional DBN.
Starting Page	1
Ending Page	4
File Size	878069
Page Count	4
File Format	PDF
ISBN	9781479910274
DOI	10.1109/ICSPCC.2013.6664137
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-05
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Emotion recognition Visualization Hidden Markov models Feature extraction Bayes methods Hidden Markov Model Dynamic Bayesian Network Dynamic model
Content Type	Text
Resource Type	Article

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