Face Recognition by Nonnegative Independent Component Analysis
Yunxia Li, Changyuan Fan · 2009
Face recognition has become one of the most active research areas of pattern recognition since the early 1990s. At present there are many face recognition algorithms. Thereinto, subspace learning method such as principal component analysis (PCA) is a very hot research topic in this field. The basis images found by PCA depend only on pairwise relationships between pixels in the image database. 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. Independent component analysis (ICA), a generalization of PCA, is one such method. In this paper the improved nonnegative ICA is performed on face images on the subjects of ORL database. The modified nonnegative ICA method can obtain good experimental results.