5 research outputs found
Iris Codes Classification Using Discriminant and Witness Directions
The main topic discussed in this paper is how to use intelligence for
biometric decision defuzzification. A neural training model is proposed and
tested here as a possible solution for dealing with natural fuzzification that
appears between the intra- and inter-class distribution of scores computed
during iris recognition tests. It is shown here that the use of proposed neural
network support leads to an improvement in the artificial perception of the
separation between the intra- and inter-class score distributions by moving
them away from each other.Comment: 6 pages, 5 figures, Proc. 5th IEEE Int. Symp. on Computational
Intelligence and Intelligent Informatics (Floriana, Malta, September 15-17),
ISBN: 978-1-4577-1861-8 (electronic), 978-1-4577-1860-1 (print
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