Viewpoint invariant face recognition using independent component analysis
and attractor networks.
Marian Stewart Bartlett and Terrence J. Sejnowski
Advances in Neural Information Processing Systems 9,
M. Mozer, M. Jordan, and T. Petsche (Eds.), MIT Press, Cambridge, MA,
1997. p. 817-823.
We have explored two approaches to recognizing faces across
changes in pose. First, we developed a representation of face images based
on independent component analysis (ICA) and compared it to a principal
component analysis (PCA) representation for face recognition. The ICA
basis vectors for this data set were more spatially local than the PCA
basis vectors and the ICA representation had greater invariance to changes
in pose. Second, we present a model for the development of viewpoint
invariant responses to faces from visual experience in a biological system.
The temporal continuity of natural visual experience was incorporated into
an attractor network model by Hebbian learning following a lowpass temporal
filter on unit activities. When combined with the temporal filter, a basic
Hebbian update rule became a generalization of Griniasty et al. (1993),
which associates temporally proximal input patterns into basins of
attraction. The system acquired representations of faces that were largely
independent of pose.