Abstract
In this paper, the robustness of appearance-based, subspace learning techniques for facial expression recognition in geometrical transformations is explored. A plethora of facial expression recognition algorithms is presented and tested using three well-known facial expression databases. Although, it is common-knowledge that appearance based methods are sensitive to image registration errors, there is no systematic experiment reported in the literature and the problem is considered, a priori, solved. However, when it comes to automatic real-world applications, inaccuracies are expected, and a systematic preprocessing is needed. After a series of experiments we observed a strong correlation between the performance and the bounding box position. The mere investigation of the bounding box’s optimal characteristics is insufficient, due to the inherent constraints a real-world application imposes, and an alternative approach is demanded. Based on systematic experiments, the database enrichment with translated, scaled and rotated images is proposed for confronting the low robustness of subspace techniques for facial expression recognition.
This work has been funded by the Collaborative European Project MOBISERV FP7-248434 (http://www.mobiserv.eu), An Integrated Intelligent Home Environment for the Provision of Health, Nutrition and Mobility Services to the Elderly.
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Bolis, D., Maronidis, A., Tefas, A., Pitas, I. (2010). Improving the Robustness of Subspace Learning Techniques for Facial Expression Recognition. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6352. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15819-3_63
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DOI: https://doi.org/10.1007/978-3-642-15819-3_63
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