Abstract
This paper presents three modular neural network architectures as systems for recognizing persons based on the iris biometric measurement of humans. In these systems, the human iris database is enhanced with image processing methods, and the coordinates of the center and radius of the iris are obtained to make a cut of the area of interest by removing the noise around the iris. The input to the modular neural networks are the processed iris images and the output is the number of the person identified. The integration of the modules was done with a gating network method.
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Gaxiola, F., Melin, P., López, M. (2010). Modular Neural Networks for Person Recognition Using the Contour Segmentation of the Human Iris Biometric Measurement. In: Melin, P., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Recognition Based on Biometrics. Studies in Computational Intelligence, vol 312. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15111-8_9
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DOI: https://doi.org/10.1007/978-3-642-15111-8_9
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