Improving the robustness of subspace learning techniques for facial expression recognition

Authors:
Dimitris Bolis;Anastasios Maronidis;Anastasios Tefas;Ioannis Pitas
Affiliations:
Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece;Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece;Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece;Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

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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.