Face recognition using ICA
In a task such as face recognition, much of the important information may be contained in the high-order relationships among the image pixels. Some success has been attained using data-driven face representations based on principal component analysis, such as "Eigenfaces" (Turk & Pentland, 1991) and "Holons" (Cottrell & Metcalfe, 1991). Principal component analysis (PCA) is based on the second-order statistics of the image set, and does not address high-order statistical dependencies such as the relationships among three or more pixels. Independent component analysis (ICA) is a generalization of PCA which separates the high-order moments of the input in addition to the second-order moments. We developed image representations based on the independent components of the face images and compared them to a PCA representation for face recognition.
ICA was performed on the face images under two different architectures. The first architecture provided a set of statistically independent basis images for the faces that can be viewed as a set of independent facial features. These ICA basis images were spatially local, unlike the PCA basis vectors. The representation consisted of the coefficients for the linear combination of basis images that comprised each face image. The second architecture produced independent coding variables (coefficients). This provided a factorial face code, in which the probability of any combination of features can be obtained from the product of their individual probabilities. The distributions of these coefficents were sparse and highly kurtotic. Classification was performed using nearest neighbor, with similarity measured as the cosine of the angle between representation vectors. Both ICA representations were superior to the PCA representation for recognizing faces across sessions, changes in expression, and changes in pose.
Papers on face image analysis using ICA by Marian Stewart Bartlett.
