@INPROCEEDINGS{1155Rama2008,
	AUTHOR = {Antonio Rama and Lutz Goldmann and Francesc Tarres and Thomas Sikora},
	TITLE = {More Robust Face Recognition by Considering Occlusion Information},
	BOOKTITLE = {8th IEEE International Conference on Automatic Face and Gesture Recognition (FG) 2008, Amsterdam, The Netherlands},
	YEAR = {2008},
	MONTH = sep,
	EDITOR = {Jeffrey F. Cohn, Thomas S. Huang, Maja Pantic, Nicu  Sebe, Ferdinand Beljaars},
	PUBLISHER = {IEEE Press},
	ORGANIZATION = {IEEE FG},
	ADDRESS = {Amsterdam, Netherlands},
	NOTE = {Poster?},
	URL = {http://www.fg2008.nl/},
	ABSTRACT = {This paper addresses one of the main challenges of face recognition (FR): facial occlusions. Currently, the human brain is the most robust known FR approach towards partially occluded faces. Nevertheless, it is still not clear if humans recognize faces using a holistic or a component-based strategy, or even a combination of both. In this paper, three different approaches based on Principal Component Analysis (PCA) are analyzed. The first one, a holistic approach, is the well-known Eigenface approach. The second one, a component-based method, is a variation of the Eigen-features approach, and finally, the third one, a near-holistic method, is an extension of the Lophoscopic Principal Component Analysis (LPCA). So the main contributions of this paper are: The three different strategies are compared and analyzed for identifying partially occluded faces and furthermore it explores how a priori knowledge about present occlusions can be used to improve the recognition performance.}
}