Emotion Recognition Using KNN Classification for User Modeling and Sharing of Affect States

In this study, we propose a new method of recognizing emotional states from physiological signals. Our proposal uses signal processing techniques to analyze physiological signals. It permits to recognize not only the basic emotions (e.g., anger, sadness, fear) but also any kind of complex emotion, including simultaneous superposed or masked emotions. This method consists of two main steps: the training step and the detection step. In the First step, our algorithm extracts the features of emotion from the data to generate an emotion training data base. Then in the second step, we apply the k-nearest-neighbor classifier to assign the predefined classes to instances in the test set. The final result is defined as an eight components vector representing emotion in multidimensional space. Experiments show the efficiency of the proposed method in detecting basic emotion by giving hight recognition rate.