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山东大学学报(工学版) ›› 2014, Vol. 44 ›› Issue (6): 70-76.doi: 10.6040/j.issn.1672-3961.0.2014.120

• 控制科学与工程 • 上一篇    下一篇

基于PCA-SVM多生理信息融合的情绪识别方法

李发权, 杨立才, 颜红博   

  1. 山东大学控制科学与工程学院, 山东 济南 250061
  • 收稿日期:2014-04-23 修回日期:2014-09-24 出版日期:2014-12-20 发布日期:2014-04-23
  • 通讯作者: 杨立才(1962-),男,山东莘县人,博士,教授,博士生导师,主要研究方向为生物医学信号的检测与处理,人工智能与智能控制等.E-mail:yanglc@sdu.edu.cn E-mail:yanglc@sdu.edu.cn
  • 作者简介:李发权(1989-),男,山东临沂人,硕士研究生,主要研究方向为生物医学信号检测分析.E-mail:limo0314@sina.com

An emotion recognition method of multiphysiological information fusion based on PCA-SVM

LI Faquan, YANG Licai, YAN Hongbo   

  1. School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China
  • Received:2014-04-23 Revised:2014-09-24 Online:2014-12-20 Published:2014-04-23

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摘要： 为了有效解决情绪识别过程中多种生理信息融合所导致的运算量过大的问题,提出了一种主成分分析(principal component analysis, PCA)与支持向量机(support vector machine, SVM)相结合的情绪识别方法。利用主成分分析法,求出各特征对情绪识别效果的影响权重,通过阈值法选择权重较大的特征组成新的特征子集,从而减少SVM的输入特征维数,降低算法的运算量。试验结果表明,该方法可以有效提高算法的执行效率。

关键词: 情绪识别, 支持向量机, 特征子集, 主成分分析, 信息融合

Abstract: To reduce the complexity of the emotion-recognition algorithm caused by multiphysiological information fusion an emotion recognition method based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) was proposed. The influential weights of emotion recognition were calculated for initial features by the PCA, and the features of which the weights were larger than a certain threshold were selected to compose the new feature set. Thus the dimension of the classifierinputs could be reduced so that the complexity of the algorithm will be simplified. Experimental results showed that the PCA-SVM algorithm for sentiment analysis could effectively improve the efficiency of emotion recognition.

Key words: information fusion, principal component analysis, support vector machine, emotion recognition, feature subset

中图分类号: 

  • TP391.3
[1] LOIC K, GINEVRA C, GEORGE C. Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis[J]. Multimodal User Interfaces, 2010, 3(1-2):33-48.
[2] 聂聃,王晓韡,段若男,等. 基于脑电的情绪识别研究综述[J]. 中国生物医学工程学报, 2012, 4(31):595-597. NIE Ran, WANG Xiaowei, DUAN Ruonan, et al. A survey on EEG based emotion recognition[J]. Chinese Journal of Biomedical Engineering, 2012, 4(31):595-597.
[3] JONGHWA K, ELISABETH A. Emotion Recognition based on physiological changes in music Listening[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 2008, 12(30):2067-2069.
[4] 牛晓伟,刘光远. 生理信号情感识别的遗传算法研究[J]. 计算机工程与应用, 2009, 45(2):233-235. NIU Xiaowei, LIU Guangyuan. Research on genetic algorithm based on emotion recognition with physiological signals[J]. Compute Engineering and Applications, 2009, 45(2):233-235.
[5] WU C, WEI W, LIN J. Speaking effect removal on emotion recognition from facial expressions based on eigenface conversion[J]. IEEE Transactions on Multimedia, 2013, 8(15):1732-1735.
[6] 刘月华. 典型生理信号综合测量及情绪识别研究[D]. 上海:上海交通大学,2011. LIU Yuehua. Typical physiological signal measurement and emotion recognition study[D]. Shanghai: Shanghai Jiaotong University, 2011.
[7] 党宏社,郭楚佳,张娜. 信息融合技术在情绪识别领域的研究展望[J]. 计算机应用研究, 2013, 12(30):3536-3538. DANG Hongshe, GUO Chujia, ZHANG Na. Research progress of emotion recognition in information fusion[J]. Application Research of Computers, 2013, 12(30):3536-3538.
[8] CHEN H,LIU G. A novel feature selection method for affective recognition based on pulse signal[C]//Proceedings of the Fourth International Symposium on Computational Intelligence and Design. Hangzhou, China:[s.n.], 2011:110-113.
[9] PARK J, KIM J, OH Y. Feature vector classification based speech emotion recognition for service robots[J]. IEEE Transactions on Consumer Electronics, 2009, 3(55):1590-1592.
[10] JOHANNES W, FLORIAN L, ELISABETH A, et al. Exploring fusion methods for multimodal emotion recognition with missing data[J]. IEEE Transactions on Affective Computing, 2011, 4(2):206-212.
[11] 孙洪央. 基于多生理信号的压力状态下情绪识别方法研究[D]. 上海:上海交通大学,2013. SUN Hongyang. Study on emotion recognition methods of stress state based on physiological signals[D]. Shanghai: Shanghai Jiaotong University, 2013.
[12] 计智伟,胡珉,尹建新. 特征选择算法综述[J]. 电子设计工程,2011,19(9):46-51. JI Zhiwei, HU Min, YIN Jianxin. A survey of feature selection algorithm[J]. Electronic Design Engineering, 2011, 19(9):46-51.
[13] RANI P, LIU C, SARKAR N, et al. An empirical study of machine learning techniques for affect recognition in human-robot interaction[J]. Pattern Anal Applic,2006, 9(1):58-69.
[14] 韩小孩,张耀辉. 基于主成分分析的指标权重确定方法[J].四川兵工学报, 2012, 33(10):124-126. HAN Xiaohai, ZHANG Yaohui. Method to determine the index weight based on principal component analysis[J]. Journal of Sichuan Ordnance, 2012, 33(10):124-126.
[15] QUAN C, WAN D, ZHANG B, et al. Reduce the dimensions of emotional features by principal component analysis for speech emotion recognition[C]//Proceedings of the 2013 IEEE/SICE International Symposium on System Integration.Kobe, Japan: IEEE, 2013:222-224.
[16] 徐雅静,汪远征. 主成分分析应用方法的改进[J]. 数学的实践与认识,2006,36(6):68-75. XU Yajing, WANG Yuanzheng. Improving the application of principal component analysis method[J]. Mathematics in Practice and Theory, 2006, 36(6):68-75.
[17] 丁世飞,齐丙娟,谭红艳. 支持向量机理论与算法研究综述[J]. 电子科技大学学报, 2011, 40(1):2-7. DING Shifei, QI Bingjuan, TAN Hongyan. An overview on theory and algorithm of support vector machines[J], Journal of University of Electronic Science and Technology of China, 2011, 40(1):2-7.
[18] 徐红敏,王海英. 支持向量机回归算法及其应用[J]. 北京石油化工学院学报, 2010, 18(1):62-63. XU Hongmin, WANG Haiying. The support vector machine regression algorithm and its application[J]. Journal of Beijing Institute of Petro-chemical Technology, 2010, 18(1):62-63.
[19] PANAGIOTIS C, LEONTIOS H. A novel emotion elicitation index using frontal brain asymmetry for enhanced EEG-based emotion recognition[J]. IEEE Transactions on Information Technology in Biomedicine, 2011, 5(15):737-743.
[20] 杨瑞请,刘光远. 基于BPSO的四种生理信号的情感状态识别[J]. 计算机科学, 2008, 35(3):137-138. YANG Ruiqing, LIU Guangyuan. Emotion recognition using four physiological signals based on BPSO[J]. Computer Science, 2008, 35(3):137-138.
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