Addressing Subject-dependency for Affective Signal Processing - Modeling Subjects’ Idiosyncracies

François Courtemanche, Emma Campbell, Pierre-Majorique Léger, Franco Lepore

Abstract

Most works on Affective Signal Processing (ASP) focus on user-dependent emotion recognition models which are personalized to a specific subject. As these types of approach have good accuracy rates, they cannot easily be reused with other subjects for industrial or research purposes. On the other hand, the reported accuracy rates of user-independent models are substantially lower. This performance decrease is mostly due to the greater variance in the physiological training data set drawn from multiple users. In this paper, we propose an approach to address this problem and enhance the performance of user-independent models by explicitly modeling subjects’ idiosyncrasies. As a first exemplification, we describe how personality traits can be used to improve the accuracy of user-independent emotion recognition models. We also present the experiment that will be carried on to validate the proposed approach.

References

  1. Alzoubi, O., D'mello, S. K. & Calvo, R. A. 2012. Detecting Naturalistic Expressions of Nonbasic Affect Using Physiological Signals. IEEE Transactions on Affective Computing, 3, 298-310.
  2. Bailenson, J. N., Pontikakis, E. D., Mauss, I. B., Gross, J. J., Jabon, M. E., Hutcherson, C. a. C., Nass, C. & John, O. 2008. Real-time classification of evoked emotions using facial feature tracking and physiological responses. International Journal of Human-Computer Studies, 66, 303-317.
  3. Bamidis, P., Frantzidis, C., Konstantinidis, E., Luneski, A., Lithari, C., Klados, M., Bratsas, C., Papadelis, C. & Pappas, C. 2009. An Integrated Approach to Emotion Recognition for Advanced Emotional Intelligence. In: JACKO, J. (ed.) Human-Computer Interaction. Ambient, Ubiquitous and Intelligent Interaction. Springer Berlin / Heidelberg.
  4. Bishop, C. M. 2006. Pattern Recognition and Machine Learning, New York, Springer.
  5. Bock, R., Gluge, S., Wendemuth, A., Limbrecht, K., Walter, S., Hrabal, D. & Traue, H. C. Intraindividual and interindividual multimodal emotion analyses in Human-Machine-Interaction. Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 2012 IEEE International MultiDisciplinary Conference on, 6-8 March 2012 2012. 59-64.
  6. Cacioppo, J. T. & Tassinary, L. G. 1990. Inferring psychological significance from physiological signals. American Psychologist, 45, 16-28.
  7. Chanel, G., Kierkels, J. J. M., Soleymani, M. & Pun, T. 2009. Short-term emotion assessment in a recall paradigm. International Journal of Human-Computer Studies, 67, 607-627.
  8. Chang, C.-Y., Chang, C.-W., Zheng, J.-Y. & Chung, P.-C. 2013. Physiological emotion analysis using support vector regression. Neurocomputing, 122, 79-87.
  9. Cheng, B. 2012. Emotion recognition from physiological signals using support vector machine. Software Engineering and Knowledge Engineering: Theory and Practice. Springer.
  10. Christie, I. C. & Friedman, B. H. 2004. Autonomic specificity of discrete emotion and dimensions of affective space: a multivariate approach. International Journal of Psychophysiology, 51, 143-153.
  11. Crider, A. 2008. Personality and Electrodermal Response Lability: An Interpretation. Applied Psychophysiology and Biofeedback, 33, 141-148.
  12. Dongrui, W., Christopher, G. C., Brent, J. L., Shrikanth, S. N., Michael, E. D., Kelvin, S. O. & Thomas, D. P. 2010. Optimal Arousal Identification and Classification for Affective Computing Using Physiological Signals: Virtual Reality Stroop Task. IEEE Transactions on Affective Computing, 1, 109- 118.
  13. Fairclough, S. H. 2009. Fundamentals of physiological computing. Interacting with Computers, 21, 133-145.
  14. Frantzidis, C. A., Bratsas, C., Klados, M. A., Konstantinidis, E., Lithari, C. D., Vivas, A. B., Papadelis, C. L., Kaldoudi, E., Pappas, C. & Bamidis, P. D. 2010. On the Classification of Emotional Biosignals Evoked While Viewing Affective Pictures: An Integrated Data-Mining-Based Approach for Healthcare Applications. IEEE Transactions on Information Technology in Biomedicine, 14, 309-318.
  15. Haag, A., Goronzy, S., Schaich, P. & Williams, J. 2004. Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System. In: ANDRÉ, E., DYBKJAE R, L., MINKER, W. & HEISTERKAMP, P. (eds.) Affective dialogue systems. Springer Berlin / Heidelberg.
  16. Hristova, E., Grinberg, M. & Lalev, E. 2009. Biosignal Based Emotion Analysis of Human-Agent Interactions. In: ESPOSITO, A. & VÍCH, R. (eds.) Cross-Modal Analysis of Speech, Gestures, Gaze and Facial Expressions. Springer Berlin / Heidelberg.
  17. Johannes, B. & Gaillard, A. W. 2014. A methodology to compensate for individual differences in psychophysiological assessment. Biological psychology, 96, 77-85.
  18. Kim, K., Bang, S. & Kim, S. 2004. Emotion recognition system using short-term monitoring of physiological signals. Medical & biological engineering & computing, 42, 419-427.
  19. Kolodyazhniy, V., Kreibig, S. D., Gross, J. J., Roth, W. T. & Wilhelm, F. H. 2011. An affective computing approach to physiological emotion specificity: Toward subject-independent and stimulus-independent classification of film-induced emotions. Psychophysiology, 48, 908-922.
  20. Kukolja, D., Popovic, S., Horvat, M., Kovac, B. & Cosic, K. 2014. Comparative analysis of emotion estimation methods based on physiological measurements for real-time applications. International Journal of Human-Computer Studies.
  21. Lang, P. J., Bradley, M. M. & Cuthbert, B. N. 2008. International affective picture system (IAPS): Affective ratings of pictures and instruction manual. Technical report B-3. University of Florida, Gainesville, FI.
  22. Lee, K. & Ashton, M. C. 2004. Psychometric Properties of the HEXACO Personality Inventory. Multivariate Behavioral Research, 39, 329-358.
  23. Marwitz, M. & Stemmler, G. 1998. On the status of individual response specificity. Psychophysiology, 35, 1-15.
  24. Myrtek, M. 1998. Metaanalysen zur psychophysiologischen persönlichkeitsforschyung [Meta-analysis for psychophysiological personality research]. In: RÖSLER, F. (ed.) Ergebnisse und Anwendungen der Psychophysiologie. Göttingen: Hogrefe Verlag für Psychologie.
  25. Nejtek, V. A. 2002. High and low emotion events influence emotional stress perceptions and are associated with salivary cortisol response changes in a consecutive stress paradigm. Psychoneuroendocrinology, 27, 337-352.
  26. Novak, D., Mihelj, M. & Munih, M. 2012. A survey of methods for data fusion and system adaptation using autonomic nervous system responses in physiological computing. Interacting with Computers, 24, 154-172.
  27. Picard, R. W., Vyzas, E. & Healey, J. 2001. Toward Machine Emotional Intelligence: Analysis of Affective Physiological State. IEEE Transactions on Pattern Analysis & Machine Intelligence, 23, 1175.
  28. Rani, P., Liu, C., Sarkar, N. & Vanman, E. 2006. An empirical study of machine learning techniques for affect recognition in human-robot interaction. Pattern Analysis & Applications, 9, 58-69.
  29. Russell, J. A. 1980. A circumplex model of affect. Journal of Personality and Social Psychology, 39, 1161-1178.
  30. Schuster, T., Gruss, S., Rukavina, S., Walter, S. & Traue, H. C. EEG-based Valence Recognition: What do we Know About the influence of Individual Specificity? The Fourth International Conference on Advanced Cognitive Technologies and Applications (COGNITIVE 2012), 2012 Nice, France. 71-76.
  31. Stemmler, G. 1997. Selective activation of traits: Boundary conditions for the activation of anger. Personality and Individual Differences, 22, 213-233.
  32. Van Den Broek, E., Janssen, J., Westerink, J. & Healey, J. Prerequisites for Affective Signal Processing (ASP). In: ENCARNAÇÃO, P. & VELOSO, A., eds. International Conference on Bio-inspired Systems and Signal Processing, 2009a Porto, Portugal. INSTICC Press, 426-433.
  33. Van Den Broek, E., Janssen, J. H., Zwaag Van Der, M. D. & Healey, J. A. Prerequisites for Affective Signal Processing (ASP) - Part III. Third International Conference on Bio-Inspired Systems and Signal Processing, Biosignals 2010, 2010a Valencia, Spain.
  34. Van Den Broek, E., Janssen, J. H. A., Healey, J. A. & Zwaag Van Der, M. 2010b. Prerequisites for Affective Signal Processing (ASP) - Part II. Third International Conference on Bio-Inspired Systems and Signal Processing, Biosignals 2010. Valencia, Spain.
  35. Van Den Broek, E., Schut, M. H., Westerink, J. H. D. M. & Tuinenbreijer, K. 2009b. Unobtrusive Sensing of Emotions (USE). J. Ambient Intell. Smart Environ., 1, 287-299.
  36. Verhoef, T., Lisetti, C., Barreto, A., Ortega, F., Van Der Zant, T. & Cnossen, F. 2009. Bio-sensing for Emotional Characterization without Word Labels. In: JACKO, J. (ed.) Human-Computer Interaction. Ambient, Ubiquitous and Intelligent Interaction. Springer Berlin / Heidelberg.
  37. Verma, G. K. & Tiwary, U. S. 2013. Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals. NeuroImage.
  38. Villon, O. & Lisetti, C. Toward Recognizing Individual's Subjective Emotion from Physiological Signals in Practical Application. Twentieth IEEE International Symposium on Computer-Based Medical Systems, 2007. CBMS 7807., 20-22 June 2007 2007. 357-362.
  39. Zhou, F., Qu, X., Helander, M. G. & Jiao, J. 2011. Affect prediction from physiological measures via visual stimuli. International Journal of Human-Computer Studies, 69, 801-819.
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Paper Citation


in Harvard Style

Courtemanche F., Campbell E., Léger P. and Lepore F. (2015). Addressing Subject-dependency for Affective Signal Processing - Modeling Subjects’ Idiosyncracies . In Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-085-7, pages 72-77. DOI: 10.5220/0005330700720077


in Bibtex Style

@conference{phycs15,
author={François Courtemanche and Emma Campbell and Pierre-Majorique Léger and Franco Lepore},
title={Addressing Subject-dependency for Affective Signal Processing - Modeling Subjects’ Idiosyncracies},
booktitle={Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS,},
year={2015},
pages={72-77},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005330700720077},
isbn={978-989-758-085-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - Addressing Subject-dependency for Affective Signal Processing - Modeling Subjects’ Idiosyncracies
SN - 978-989-758-085-7
AU - Courtemanche F.
AU - Campbell E.
AU - Léger P.
AU - Lepore F.
PY - 2015
SP - 72
EP - 77
DO - 10.5220/0005330700720077