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Face Recognition by Dynamic Link Matching
Laurenz Wiskott
and
Christoph von der Malsburg
Institut für Neuroinformatik
Ruhr Universität Bochum
D-44780 Bochum, Germany
wiskott@salk.edu,malsburg@neuroinformatik.ruhr-uni-bochum.de
Abstract
We present a neural system for the recognition of objects from
realistic images, together with results of tests of face recognition
from a large gallery. The system is inherently invariant with
respect to shift, and is robust against many other variations, most
notably rotation in depth and deformation. The system is based on
Dynamic Link Matching. It consists of an image domain and a model
domain, which we tentatively identify with primary visual cortex and
infero-temporal cortex. Both domains have the form of neural sheets
of hypercolumns, which are composed of simple feature detectors
(modeled as Gabor-based wavelets). Each object is represented in
memory by a separate model sheet, that is, a two-dimensional array of
features. The match of the image to the models is performed by
network self-organization, in which rapid reversible synaptic
plasticity of the connections (``dynamic links'') between the two
domains is controlled by signal correlations, which are shaped by
fixed inter-columnar connections and by the dynamic links themselves.
The system requires very little genetic or learned structure, relying
essentially on the rules of rapid synaptic plasticity and the a
priori constraint of preservation of topography to find matches.
This constraint is encoded within the neural sheets with the help of
lateral connections, which are excitatory over short range and
inhibitory over long range.
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