Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/7192
Title: Improving subspace learning for facial expression recognition using person dependent and geometrically enriched training sets
Authors: Bolis, Dimitris
Tefas, Anastasios
Pitas, Ioannis K.
Maronidis, Anastasios
Keywords: Facial expression
Image registration
Experiments
Issue Date: 2011
Publisher: Elsevier
Source: Neural Networks, 2011, Volume 24, Issue 8, Pages 814–823
Abstract: In this paper, the robustness of appearance-based subspace learning techniques in geometrical transformations of the images is explored. A number of such techniques are presented and tested using four facial expression databases. A strong correlation between the recognition accuracy and the image registration error has been observed. Although it is common-knowledge that appearance-based methods are sensitive to image registration errors, there is no systematic experiment reported in the literature. As a result of these experiments, the training set enrichment with translated, scaled and rotated images is proposed for confronting the low robustness of these techniques in facial expression recognition. Moreover, person dependent training is proven to be much more accurate for facial expression recognition than generic learning.
URI: http://ktisis.cut.ac.cy/handle/10488/7192
ISSN: 08936080
DOI: 10.1016/j.neunet.2011.05.015
Rights: © 2011 Elsevier Ltd.
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