Multiple classifier systems for the classificatio of audio-visual emotional states

  • Authors:

  • Michael Glodek;Stephan Tschechne;Georg Layher;Martin Schels;Tobias Brosch;Stefan Scherer;Markus Kächele;Miriam Schmidt;Heiko Neumann;Günther Palm;Friedhelm Schwenker

  • Affiliations:

  • Ulm University, Institute of Neural Information Processing, Germany;Ulm University, Institute of Neural Information Processing, Germany;Ulm University, Institute of Neural Information Processing, Germany;Ulm University, Institute of Neural Information Processing, Germany;Ulm University, Institute of Neural Information Processing, Germany;Ulm University, Institute of Neural Information Processing, Germany;Ulm University, Institute of Neural Information Processing, Germany;Ulm University, Institute of Neural Information Processing, Germany;Ulm University, Institute of Neural Information Processing, Germany;Ulm University, Institute of Neural Information Processing, Germany;Ulm University, Institute of Neural Information Processing, Germany

  • Venue:
  • ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
  • Year:
  • 2011

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Abstract

Research activities in the field of human-computer interaction increasingly addressed the aspect of integrating some type of emotional intelligence. Human emotions are expressed through different modalities such as speech, facial expressions, hand or body gestures, and therefore the classification of human emotions should be considered as a multimodal pattern recognition problem. The aim of our paper is to investigate multiple classifier systems utilizing audio and visual features to classify human emotional states. For that a variety of features have been derived. From the audio signal the fundamental frequency, LPCand MFCC coefficients, and RASTA-PLP have been used. In addition to that two types of visual features have been computed, namely form and motion features of intermediate complexity. The numerical evaluation has been performed on the four emotional labels Arousal, Expectancy, Power, Valence as defined in the AVEC data set. As classifier architectures multiple classifier systems are applied, these have been proven to be accurate and robust against missing and noisy data.