5 research outputs found
Examples of Artificial Perceptions in Optical Character Recognition and Iris Recognition
This paper assumes the hypothesis that human learning is perception based,
and consequently, the learning process and perceptions should not be
represented and investigated independently or modeled in different simulation
spaces. In order to keep the analogy between the artificial and human learning,
the former is assumed here as being based on the artificial perception. Hence,
instead of choosing to apply or develop a Computational Theory of (human)
Perceptions, we choose to mirror the human perceptions in a numeric
(computational) space as artificial perceptions and to analyze the
interdependence between artificial learning and artificial perception in the
same numeric space, using one of the simplest tools of Artificial Intelligence
and Soft Computing, namely the perceptrons. As practical applications, we
choose to work around two examples: Optical Character Recognition and Iris
Recognition. In both cases a simple Turing test shows that artificial
perceptions of the difference between two characters and between two irides are
fuzzy, whereas the corresponding human perceptions are, in fact, crisp.Comment: 5th Int. Conf. on Soft Computing and Applications (Szeged, HU), 22-24
Aug 201
Exploratory simulation of an Intelligent Iris Verifier Distributed System
This paper discusses some topics related to the latest trends in the field of
evolutionary approaches to iris recognition. It presents the results of an
exploratory experimental simulation whose goal was to analyze the possibility
of establishing an Interchange Protocol for Digital Identities evolved in
different geographic locations interconnected through and into an Intelligent
Iris Verifier Distributed System (IIVDS) based on multi-enrollment. Finding a
logically consistent model for the Interchange Protocol is the key factor in
designing the future large-scale iris biometric networks. Therefore, the
logical model of such a protocol is also investigated here. All tests are made
on Bath Iris Database and prove that outstanding power of discrimination
between the intra- and the inter-class comparisons can be achieved by an IIVDS,
even when practicing 52.759.182 inter-class and 10.991.943 intra-class
comparisons. Still, the test results confirm that inconsistent enrollment can
change the logic of recognition from a fuzzified 2-valent consistent logic of
biometric certitudes to a fuzzified 3-valent inconsistent possibilistic logic
of biometric beliefs justified through experimentally determined probabilities,
or to a fuzzified 8-valent logic which is almost consistent as a biometric
theory - this quality being counterbalanced by an absolutely reasonable loss in
the user comfort level.Comment: 4 pages, 2 figures, latest version: http://fmi.spiruharet.ro/bodorin