  A novel human emotion recognition method based on automatically selected
Galvanic Skin Response (GSR) signal features and SVM is proposed in this paper.
GSR signals were acquired by e-Health Sensor Platform V2.0. Then, the data is
de-noised by wavelet function and normalized to get rid of the individual
difference. 30 features are extracted from the normalized data, however,
directly using of these features will lead to a low recognition rate. In order
to gain the optimized features, a covariance based feature selection is
employed in our method. Finally, a SVM with input of the optimized features is
utilized to achieve the human emotion recognition. The experimental results
indicate that the proposed method leads to good human emotion recognition, and
the recognition accuracy is more than 66.67%.

Signal Processing;Human-Computer Interaction;Machine Learning