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Real-Time Emotion Recognition: An Improved Hybrid Approach for Classification Performance

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Intelligent Computing Theory (ICIC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8588))

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Abstract

Emotions, and more in detail facial emotions, play a crucial role in human communication. While for humans the recognition of facial states and their changes is automatic and performed in real-time, for machines the modeling and the emulation of this natural process through computer vision-based approaches are still a challenge, since real-time and automation system requirements negatively affect the accuracy in emotion detection processes.

In this work, we propose an approach which improves the classification performance of our previous computer vision-based algorithm for facial feature extraction and automatic emotion recognition. The proposed approach integrates the previous one adding six geometrical and two appearance-based features, still meeting the real-time requirement. As result, we obtain an improved processing pipeline classifier (classification accuracy incremented up to 6-7%) which allows the recognition of eight facial emotions (six basic Ekman’s emotions plus Contemptuous and Neutral).

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Author information

Authors and Affiliations

  1. PERCRO, TeCIP Scuola Superiore Sant’Anna, Pisa, Italy

    Claudio Loconsole & Antonio Frisoli

  2. Dipartimento di Ingegneria Elettrica e dell’Informazione (DEI), Politecnico di Bari, Bari, Italy

    Domenico Chiaradia & Vitoantonio Bevilacqua

Authors
  1. Claudio Loconsole
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  2. Domenico Chiaradia
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  3. Vitoantonio Bevilacqua
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  4. Antonio Frisoli
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Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. Electrical and Electronics Department, Politecnico of Bari, Via Orabona, 4,, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  3. School of Electrical, Computer and Telecommunications Engineering, University of Wollongon, 2522, North Wollongon, NSW, Australia

    Prashan Premaratne

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Loconsole, C., Chiaradia, D., Bevilacqua, V., Frisoli, A. (2014). Real-Time Emotion Recognition: An Improved Hybrid Approach for Classification Performance. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_35

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  • DOI: https://doi.org/10.1007/978-3-319-09333-8_35

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09332-1

  • Online ISBN: 978-3-319-09333-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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