Design and Implementation of HMM for 3D Emotion Recognition
Keywords:
Hidden Markov model, emotion recognition, facial expressionAbstract
Facial expression is one of the most useful information in human robot interaction. To improve the accuracy in 3-dimension based facial expression recognition, Hidden Markov Models (HMMs) are used to recognize the emotion from facial expressions in this study. In particular, facial expressions are measured by two parameters, which are given by previous work. The human emotions are defined as: anger, smile, normal, sadness, fear, and surprise. The referred parts in human face are selected based on the activeness during the facial expression. The activity and arousal values of each facial part are used as the observations for each hidden state in HMMs. BaumWelch algorithm is used to train the hidden Markov model. As a result, six different emotions are very efficiently recognized through the trained HMMs.
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