A. J. Colmenarez, B. Frey, and T. S. Huang. Detection and tracking of
faces and facial features. In Proc. International Conference on Image
 L. Sirovich and M. Meytlis. Symmetry, probability, and recognition in
face space. PNAS : Proceedings of the National Academy of Sciences,
 M. H. Yang. Kernel eigenfaces vs. kernel ﬁsherfaces: Face recognition
using kernel methods. Proc. IEEE International Conference on
Automatic Face and Gesture Recognition, pages 215–220, 2002.
 A. Rosenfeld W. Zhao, R. Chellappa and P. J. Phillips. Face recognition:
A literature survey. ACM Computing Surveys, pages 399–458, 2003.
 D. K. Baek and J. R. Beveridge. Pca vs. ica: A comparison on the feret
data set. Proc. of the Fourth International Conference on Computer
Vision, Pattern Recognition and Image processing, pages 824–827, 2002.
 H. Lu Q. Liu, R. Huang and S. Ma. Face recognition using kernel based
ﬁsher discriminant analysis. Proc. of the ﬁfth Int. Conf. on Automatic
Face and Gesture Recognition, Washington DC, 2002.
 S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by
locally linear embedding. Science, 290, 2000.
 G. Shakhnarovich and B. Moghaddam. Face recognition in subspaces.
Handbook of Face Recognition, Eds. S. Z. Li and A. K. Jain, Springer-
Verlag, page 35, 2004.
 X. Lu. Image analysis for face recognition. Personal notes, page 36,
 A. Martinez and A. Kak. Pca versus lda. IEEE Trans. on Pattern
Analysis and Machine Intelligence, 23:228–233, 2001.
 P. Navarrete and J. R. D. Solar. Analysis and comparison of eigenspace-
based face recognition approaches. International Journal of Pattern
Recognition and Artiﬁcial Intelligence, 16:817–830, 2002.
 X. Wang and X. Tang. A uniﬁed framework for subspace face
recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence,
26, 2004, pages=1222-1228 ,.
 M. Grgic K. Delac and S. Grgic. Data independent comparative study
of pca, ica, and lda on the feret set. International Journal of Imaging
Systems and Technology, 15(5).
 B. Moghaddam A. Pentland and T. Starner. View-based and modular
eigenspaces for face recognition. IEEE Computer Conference on
Computer Vision and Pattern Recognition, pages 84–91, Jun. 1994.