Regression algorithm for emotion detection

We present two components of a computational system for emotion detection. PEMs (Personalized Emotion Maps) store links between bodily expressions and emotion values, and are individually calibrated to capture each person's emotion profile. They are an implementation based on aspects of Scherer's theoretical complex system model of emotion [1], [2]. We also present a regression algorithm that determines a person's emotional feeling from sensor measurements of their bodily expressions, using their individual PEMs. The aim of this architecture is to dissociate sensor measurements of bodily expression from the emotion expression interpretation, thus allowing flexibility in the choice of sensors. We test the prototype system using video sequences of facial expressions and demonstrate the real-time capabilities of the system for detecting emotion. We note that, interestingly, the system displays the sort of hysteresis phenomenon in changing emotional state as suggested by Scherer's psychological model.

[1]  P. Kleinginna,et al.  A categorized list of emotion definitions, with suggestions for a consensual definition , 1981 .

[2]  Marc Schröder,et al.  Piecing Together the Emotion Jigsaw , 2004, MLMI.

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

[4]  Klaus R Scherer,et al.  Emotions are emergent processes: they require a dynamic computational architecture , 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

[5]  P. Ekman.  Universals and cultural differences in facial expressions of emotion. , 1972 .

[6]  Franck Berthelon.  Modélisation et détection des émotions à partir de données expressives et contextuelles. (Emotion modelization and detection from expressive and contextual data) , 2013 .

[7]  R. Thom.  Stabilité structurelle et morphogenèse , 1974 .

[8]  Klaus R. Scherer,et al.  Emotion, Development, and Self-Organization: Emotions as Episodes of Subsystem Synchronization Driven by Nonlinear Appraisal Processes , 2000 .

[9]  J. Russell.  A circumplex model of affect. , 1980 .

[10]  Ryohei Nakatsu,et al.  Emotion Recognition in Speech Using Neural Networks , 2000, Neural Computing & Applications.

[11]  Bao-Liang Lu,et al.  Emotion classification based on gamma-band EEG , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Peter Baranyi,et al.  Cognitive infocommunications: CogInfoCom , 2010, 2010 11th International Symposium on Computational Intelligence and Informatics (CINTI).

[13]  E. Vesterinen,et al.  Affective Computing , 2009, Encyclopedia of Biometrics.

[14]  W. Cannon.  The James-Lange theory of emotions: a critical examination and an alternative theory. By Walter B. Cannon, 1927. , 1927, The American journal of psychology.