NMF vs ICA for face recognition

This paper deals with the application of spatially localized, nonoverlapping features for face recognition. The analysis is carried out by using the features generated from two closely related techniques known as independent component analysis (ICA) and nonnegative matrix factorization (NMF). A set of statistically independent basis vectors with sparse features is derived from ICA. Likewise, NMF is used to yield sparse representation of localized features to represent distributed parts over a human face. Similarities between reconstructed faces of test images and a set of synthesised face representations from the basis vectors derived from an image database using the two techniques are measured. The strengths and weaknesses of each method in the context of face recognition are discussed.