A biometric signature based system for improved emotion recognition using physiological responses from multiple subjects

Emotion awareness has become one of the key characteristics in human-computer interactions in order to achieve more natural and intelligent communications. As an alternative channel for emotional communication, physiological signals have gradually earned attentions in the field of affective computing. Ever since Dr. Picard and colleagues brought forward the initial concept of physiological signals based emotion recognition, various studies have been reported following the same system structure, which tends to perform worse when dealing with multiple subjects. In this paper, we proposed a novel biometric signature based system that is able to improve the performance of multi-subject emotion recognition. The basic idea is to transform a subject-independent case into several subject-dependent cases by using mixture multivariate t-distributions and Maximum a Posteriori (MAP) rule to remove the influence of individual varieties. The result shows great improvement compared to the traditional subject-independent procedure.

[1]  Fatma Nasoz,et al.  Emotion Recognition from Physiological Signals for Presence Technologies , 2004 .

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

[3]  Takumi Ichimura,et al.  Emotion Analyzing Method Using Physiological State , 2004, KES.

[4]  Rosalind W. Picard.  Affective Computing , 1997 .

[5]  Georgios C. Anagnostopoulos,et al.  Knowledge-Based Intelligent Information and Engineering Systems , 2003, Lecture Notes in Computer Science.

[6]  Jason Williams,et al.  Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System , 2004, ADS.

[7]  P. Lang.  International affective picture system (IAPS) : affective ratings of pictures and instruction manual , 2005 .

[8]  L. Qu,et al.  Using GA-based feature selection for emotion recognition from physiological signals , 2009, 2008 International Symposium on Intelligent Signal Processing and Communications Systems.

[9]  D. Rubin,et al.  ML ESTIMATION OF THE t DISTRIBUTION USING EM AND ITS EXTENSIONS, ECM AND ECME , 1999 .

[10]  Rangaraj M. Rangayyan,et al.  Biomedical Signal Analysis: A Case-Study Approach , 2001 .

[11]  Shy Shoham,et al.  Robust clustering by deterministic agglomeration EM of mixtures of multivariate t-distributions , 2002, Pattern Recognit..

[12]  S. Tomkins,et al.  Affect Imagery Consciousness: The Positive Affects , 1963 .

[13]  J. Gross,et al.  Emotion elicitation using films , 1995 .

[14]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[15]  Yuan Gu,et al.  Emotion-aware technologies for consumer electronics , 2008, 2008 IEEE International Symposium on Consumer Electronics.

[16]  Student,et al.  THE PROBABLE ERROR OF A MEAN , 1908 .

[17]  Geoffrey J. McLachlan,et al.  Robust mixture modelling using the t distribution , 2000, Stat. Comput..

[18]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[19]  Johannes Wagner,et al.  From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[20]  Elisabeth André,et al.  Emotion recognition based on physiological changes in music listening , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.