Abstract
This paper presents an efficient system for recognizing the identity of a living person on the basis of iris patterns that are one of the physiological and biological features with high reliability. After various preprocessing are conducted for the iris data acquired by a CCD camera and an image grabber, feature vectors are extracted using Wavelet transform. A competitive learning neural network is used to classify the patterns. To represent the iris pattern efficiently, a new method for optimizing the dimension of feature vectors is proposed without any influence to the system performance. In order to increase the recognition accuracy of competitive learning algorithm, an efficient initialization of the weight vectors and a new method to determine the winner are also proposed. With all of these novel mechanisms, the experimental results showed that the proposed system could be used for personal identification in an efficient and effective manner.
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© 2000 Springer-Verlag Berlin Heidelberg
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Lim, S., Lee, K., Byeon, O., Kim, T. (2000). Efficient Iris Recognition System by Optimization of Feature Vectors and Classifier. In: Mizoguchi, R., Slaney, J. (eds) PRICAI 2000 Topics in Artificial Intelligence. PRICAI 2000. Lecture Notes in Computer Science(), vol 1886. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44533-1_20
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DOI: https://doi.org/10.1007/3-540-44533-1_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67925-7
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