Real-Time Facial Expression Recognition Based on Boosted Embedded Hidden Markov Model
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
Buried Markov models for speech recognition
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
A Clustering Based Bayesian Network Classifier
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01
Robust Discriminant Analysis of Gabor Feature for Face Recognition
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 03
Local Gabor Fisher Classifier for Face Recognition
ICIG '07 Proceedings of the Fourth International Conference on Image and Graphics
Automatic facial expression recognition using facial animation parameters and multistream HMMs
IEEE Transactions on Information Forensics and Security
An SVM-AdaBoost facial expression recognition system
Applied Intelligence
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To overcome the disadvantage of classical recognition model that cannot perform well enough when there are some noises or lost frames in expression image sequences, a novel model called fuzzy buried Markov model (FBMM) is presented in this paper. FBMM relaxes conditional independence assumptions for classical hidden Markov model (HMM) by adding the specific cross-observation dependencies between observation elements. Compared with buried Markov model (BMM), FBMM utilizes cloud distribution to replace probability distribution to describe state transition and observation symbol generation and adopts maximum mutual information (MMI) method to replace maximum likelihood (ML) method to estimate parameters. Theoretical justifications and experimental results verify higher recognition rate and stronger robustness of facial expression recognition for image sequences based on FBMM than those of HMM and BMM.