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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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
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