Independent components of face images: A representation for face recognition
Marian Stewart Bartlett and Terrence J. Sejnowski
Proceedings of the 4th Annual Jount Symposium on Neural Computation,
Pasadena, CA, May 17, 1997. Proceedings can be obtained from the Institute
for Neural Computation, UCSD 0523, La Jolla, CA 92093.
Abstract
Methods for obtaining representations of face images based on independent
component analysis (ICA) are presented. A global ICA representation is
compared to a global representation based on principal component analysis
(PCA) for recognizing faces across changes in lighting and changes in pose.
For each set of face images, a set of statistically independent source
images was found through an unsupervised learning algorithm that maximized
the mutual information between the input and the output of a nonlinear
transformation (Bell \& Sejnowski, 1995). These source images comprised
the kernels for the representation. The independent component kernels gave
superior class discriminability to the principal component kernels.
Recognition across changes in pose with the ICA representation was 93\%,
compared to 87\% with a PCA representation, and across changes in lighting
ICA gave 100\% correct recognition, compared to 90\% with PCA.