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• W2224722700 abstract "In this paper, we propose a 3D HMM (Three-dimensional Hidden Markov Models) approach to recognizing human facial expressions and associated emotions. Human emotion is usually classified by psychologists into six categories: Happiness, Sadness, Anger, Fear, Disgust and Surprise. Further, psychologists categorize facial movements based on the muscles that produce those movements using a Facial Action Coding System (FACS).  We look beyond pure muscle movements and investigate facial features – brow, mouth, nose, eye height and facial shape – as a means of determining associated emotions. Histogram of Optical Flow is used as the descriptor for extracting and describing the key features, while training and testing are performed on 3D Hidden Markov Models. Experiments on datasets show our approach is promising and robust." @default.
• W2224722700 created "2016-06-24" @default.
• W2224722700 creator A5012408243 @default.
• W2224722700 creator A5056796999 @default.
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• W2224722700 date "2015-12-30" @default.
• W2224722700 modified "2023-09-26" @default.
• W2224722700 title "3D HMM-based Facial Expression Recognition using Histogram of Oriented Optical Flow" @default.
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• W2224722700 doi "https://doi.org/10.14738/tmlai.36.1661" @default.
• W2224722700 hasPublicationYear "2015" @default.
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