Extended Hidden Markov Model for Recognition of Facial Expression
Jieyu Zhao · Journal of Ningbo University · 2008
The paper introduces an approach of facial expression recognition using Extended Hidden Markov Model(E-HMM) on the basis of the relation between facial expression and facial features.The proposed method can better model 2D-data than 1D-HMM with less computational complexity in comparison with the pseudo 2D-HMM.The proposed method is characterized in using multiple region of interest(multi-ROI) instead of single region and combining both spatial and temporal features with the E-HMM.The method makes use of an optimized set of observation vectors obtained from the 2D-DCT coefficients of the facial region of interest.The E-HMM is trained using segmental K-means algorithm and then used for the facial expression recognition.The experimental results reveal the significant system performance improvement and robustness as well.