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FPGA-based enhanced probabilistic convergent weightless network for human iris recognition

UNSPECIFIED (2009) FPGA-based enhanced probabilistic convergent weightless network for human iris recognition. In: UNSPECIFIED, ? - ?.

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Abstract

This paper investigates how human identification and identity verification can be performed by the application of an FPGA based weightless neural network, entitled the Enhanced Probabilistic Convergent Neural Network (EPCN), to the iris biometric modality. The human iris is processed for feature vectors which will be employed for formation of connectivity, during learning and subsequent recognition. The pre-processing of the iris, prior to EPCN training, is very minimal. Structural modifications were also made to the Random Access Memory (RAM) based neural network which enhances its robustness when applied in real-time.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: ESANN 2009 Proceedings, 17th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Clare Chatfield
Date Deposited: 19 Sep 2013 12:45
Last Modified: 09 Jan 2019 02:15
URI: http://repository.essex.ac.uk/id/eprint/6865

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