Aristotle University of Thessaloniki, Department of Informatics, Box 451, 54124 Thessaloniki, Greece.
Neural Netw. 2011 Oct;24(8):814-23. doi: 10.1016/j.neunet.2011.05.015. Epub 2011 Jul 21.
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.
本文探讨了基于外观的子空间学习技术在图像几何变换中的稳健性。提出并测试了多种此类技术，使用了四个面部表情数据库。已经观察到识别精度与图像配准误差之间存在很强的相关性。虽然基于外观的方法对图像配准误差敏感是常识，但文献中没有报道系统的实验。由于这些实验，提出了用翻译、缩放和旋转图像丰富训练集的方法，以应对这些技术在面部表情识别中低稳健性的问题。此外，实验证明，与通用学习相比，基于特定人的训练对于面部表情识别更为准确。
