A comparative study of SVM kernel applied to emotion recognition from physiological signals

This paper investigates the performance of support vector machines with linear, cubic and radial basis function (RBF) kernels in the problem of emotion recognition from physiological signals. Five physiological signals: blood volume pulse (BVP), electromyography (EMG), skin conductance (SC), skin temperature (SKT) and respiration (RESP) were selected to extract 30 features for recognition. Support vector machine(SVM) is a new technique for pattern classification, and is used in many applications. Kernel type in the SVM training process, along with feature selection, will significantly impact classification accuracy. Experiments are designed and carried out to find the best SVM kernel among linear, cubic, and RBF for emotions recognition. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance over six emotional states.

[1]  Jennifer Healey,et al.  Affective wearables , 1997, Digest of Papers. First International Symposium on Wearable Computers.

[2]  J. Gross,et al.  Hiding feelings: the acute effects of inhibiting negative and positive emotion. , 1997, Journal of abnormal psychology.

[3]  P. Ekman,et al.  Autonomic nervous system activity distinguishes among emotions. , 1983, Science.

[4]  Jon D. Morris.  Observations: SAM: The Self-Assessment Manikin An Efficient Cross-Cultural Measurement Of Emotional Response 1 , 1995 .

[5]  Shingo Kuroiwa,et al.  An Advanced Mental State Transition Network and Psychological Experiments , 2005, EUC.

[6]  F. Sepulveda,et al.  Neural network-based improvement in class separation of physiological signals for emotion classification , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[7]  Jennifer A. Healey,et al.  Wearable and automotive systems for affect recognition from physiology , 2000 .

[8]  Lorenzo Bruzzone,et al.  Classification of hyperspectral remote sensing images with support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[9]  P. Ekman,et al.  Voluntary facial action generates emotion-specific autonomic nervous system activity. , 1990, Psychophysiology.

[10]  Giles M. Foody,et al.  A relative evaluation of multiclass image classification by support vector machines , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Rajita Sinha,et al.  Multivariate Response Patterning of Fear and Anger , 1996 .

[12]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  A. Angrilli,et al.  Cardiac responses associated with affective processing of unpleasant film stimuli. , 2000, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.