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). © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Loconsole, C., Chiaradia, D., Bevilacqua, V., & Frisoli, A. (2014). Real-time emotion recognition: An improved hybrid approach for classification performance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8588 LNCS, pp. 320–331). Springer Verlag. https://doi.org/10.1007/978-3-319-09333-8_35
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