Emotion Recognition System by Gesture Analysis Using Fuzzy Sets

Gestures have been called the leaky source of emotional information. Also gestures are easy to retrieve from a distance by ordinary cameras. Thus as many would agree gestures become an important clue to the emotional state of a person. In this paper we have worked on recognizing emotions of a person by analyzing only gestural information. Subjects are initially trained to perform emotionally expressive gestures by a professional actor. The same actor trained the system to recognize the emotional context of gestures. Finally the gestural performances of the subjects are evaluated by the system to identify the class of emotion indicated. Our system yields an accuracy of 94.4% with a training set of only one gesture per emotion. Apart from this our system is also computationally efficient. Our work analyses emotions from only gestures, which is a significant step towards reducing the cost efficiency of emotion recognition. It may be noted here that this system may also be used for the purpose of general gesture recognition. We have proposed new features and a new classifying approach using fuzzy sets. We have achieved state of art accuracy with minimal complexity as each motion trajectory along each axis generates only 4 displacement features. Each axis generates a trajectory and only 6 joint trajectories among all joint trajectories are compared. The 6 motion trajectories are selected based on maximum motion, as maximum moving regions give more information on gestures. The experiments have been performed on data obtained from Microsoft Kinect sensors. Training and Testing were subject gender independent.