A study on emotion recognition from body gestures using Kinect sensor

This novel work is aimed at the study of emotion recognition from gestures using Kinect sensor. The Kinect sensor along with Software Development Kit (SDK) generates the human skeleton represented by 3-dimensional coordinates corresponding to twenty body joints. Using the co-ordinates of eleven such joints from the upper body and the hands, a set of nine features based on the distances, accelerations and angles between the different joints have been extracted. These features are able to uniquely identify gestures corresponding to five basic human emotional states, namely, `Anger´, `Fear´, `Happiness´, `Sadness´ and `Relaxation´. The goal of the proposed system is to classify an emotion based on body gesture. A comparison of classification using binary decision tree, ensemble decision tree, k-nearest neighbour, support vector machine with radial basis function kernel and neural network classifier based on back-propagation learning is made, in terms of average classification accuracy and computation time. A high overall recognition rate of 90.83% is obtained from the ensemble decision tree.