Abstract
Human gait data have abundant information for recognition of actions, intentions, emotions, and gender. The paper presents an approach toward classification of human emotions using gait data into three classes: happy, angry, and normal. Data of human gait for 3 emotional expressions (happy, angry, and neutral) of 25 individuals were collected. The silhouette was divided into 9 segments in order to analyze motion in various body parts moving with different frequency. The features such as centroid, aspect ratio, and orientation were extracted from different segments using geometric and Krawtchouk moments, respectively. A train model was generated from testing data using support vector machines (SVM), and hence, new feature vector was classified into three classes. The results show that polynomial kernel using geometric moment features has the maximum recognition rate of 83.06 %.
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