We used a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons. Independent component analysis (ICA), a generalization of PCA, is one such method. In a task such as face recognition, in which important information may be contained in the high-order relationships among pixels, it seems reasonable to expect that better basis images may be found by methods sensitive to these high-order statistics. The basis images found by PCA depend only on pairwise relationships between pixels in the image database. Principal component analysis (PCA) is a popular example of such methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. A number of current face recognition algorithms use face representations found by unsupervised statistical methods.
