A regional hidden Markov model (RHMM) for automatic facial expression recognition in video sequen... more A regional hidden Markov model (RHMM) for automatic facial expression recognition in video sequences is proposed. Facial action units are described by RHMMs for the states of facial regions: eyebrows, eyes and mouth registered in a video. The tracked facial feature points in the spatial domain form observation sequences that drive the classification process. It is shown that the proposed technique outperforms other methods reported in the literature for the personindependent case tested with the extended Cohn-Kanade database.
Human facial expressions are regarded as a vital indicator of one’s emotion and intention, and ev... more Human facial expressions are regarded as a vital indicator of one’s emotion and intention, and even reveal the state of health and wellbeing. Emotional states have been associated with information processing within and between subcortical and cortical areas of the brain, including the amygdala and prefrontal cortex. In this study, we evaluated the relationship between spontaneous human facial affective expressions and multi-modal brain activity measured via non-invasive and wearable sensors: functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) signals. The affective states of twelve male participants detected via fNIRS, EEG, and spontaneous facial expressions were investigated in response to both image-content stimuli and video-content stimuli. We propose a method to jointly evaluate fNIRS and EEG signals for affective state detection (emotional valence as positive or negative). Experimental results reveal a strong correlation between spontaneous facial aff...
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2014
Human face is a display of mental states that reflect the true feelings of a person. In this pape... more Human face is a display of mental states that reflect the true feelings of a person. In this paper, we propose a framework for the video analysis of spontaneous facial expressions using an automatic facial emotion recognition system. Regional Hidden Markov Models (RHMMs) are created to describe the states of facial attributes for eyebrows, eyes, and mouth regions registered in a video sequence. The performance results reported in the paper show that the proposed technique outperforms the designated HMM for each emotion type [1, 2] tested with the Cohn-Kanade database for the person-independent case. More importantly, we used the proposed system to infer the mental states of a person based on spontaneous facial expressions. Merit of the proposed system is validated with human based evaluations.
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Papers by Yanjia Sun