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Computational framework for emotional VAD prediction using regularized Extreme Learning Machine

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Abstract

With the advancement of Human Computer interaction and affective computing, emotion estimation becomes a very interesting area of research. In literature, the majority of emotion recognition systems presents an insufficiency due to the complexity of processing a huge number of physiological data and analyzing various kind of emotions in one framework. The aim of this paper is to present a rigorous and effective computational framework for humans affect recognition and classification through arousal valence and dominance dimensions. In the proposed algorithm, physiological instances from the multimodal emotion DEAP dataset has been used for the analysis and characterization of emotional pattern. Physiological features were employed to predict VAD levels via Extreme Learning Machine. We adopted a feature-level fusion to exploit the complementary information of some physiological sensors in order to improve the classification performance. The proposed framework was also evaluated in a V–A quadrant by predicting four emotional classes. The obtained results proves the robustness and correctness of our proposed framework compared to other recent studies. We can also confirm the sufficiency of the R-ELM when it was applied for the estimation and recognition of emotional responses.

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Acknowledgements

The authors want to thank all iBUG group members and the DEAP dataset administrator for the sharing of all database files for free.

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Authors and Affiliations

  1. LR-SITI Laboratory, University Tunis El Manar, BP 37, Belvdre, 1002, Tunis, Tunisie

    Zied Guendil & Zied Lachiri

  2. LCOMS Laboratory, University of Lorraine, ISEA, 7 rue Marconi, 57070, Metz, France

    Choubeila Maaoui

Authors
  1. Zied Guendil
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  2. Zied Lachiri
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  3. Choubeila Maaoui
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Correspondence to Zied Guendil.

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Guendil, Z., Lachiri, Z. & Maaoui, C. Computational framework for emotional VAD prediction using regularized Extreme Learning Machine. Int J Multimed Info Retr 6, 251–261 (2017). https://doi.org/10.1007/s13735-017-0128-9

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  • DOI: https://doi.org/10.1007/s13735-017-0128-9

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