Automatic Human Emotion Recognition in Surveillance Video

Recognition and study of human emotions have fascinated a lot of attention in the past two decades and have been researched broadly in the field of computer vision. The recognition of complete-body expressions is significantly harder, because the pattern of the human pose has additional degrees of self-determination than the face alone, and its overall shape varies robustly during articulated motion. This chapter presents a method for emotion recognition based on the gesture dynamics features extracted from the foreground object to represent various levels of a person’s posture. The experiments are carried out using publicly available emotion recognition dataset, and the extracted motion feature set is modeled by support vector machines (SVM), Naive Bayes, and dynamic time wrapping (DTW) which are used to classify the human emotions. Experimental results show that DTW is efficient in recognizing the human emotion with an overall recognition accuracy of 93.39 %, when compared to SVM and Naive Bayes.

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