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%.

翻译：本文提出了一种基于自动选择的皮肤电反应（GSR）信号特征与支持向量机（SVM）的新型人体情绪识别方法。通过e-Health传感器平台V2.0采集GSR信号，随后利用小波函数对数据进行去噪处理并归一化以消除个体差异。从归一化数据中提取30个特征，但直接使用这些特征会导致识别率偏低。为获取优化特征，本方法采用基于协方差的特征选择技术。最后，以优化特征作为输入的SVM实现人体情绪识别。实验结果表明，所提方法能有效实现人体情绪识别，识别准确率超过66.67%。