Abstract
In order to ensure that the performance of a fingerprint recognition system will be powerful with respect to the quality of input fingerprint images, the enhancement of fingerprints is essential. In this study wavelet transform and contourlet transform which is a new extension of the wavelet transform in two dimensions are applied for fingerprint enhancement. In addition, feature selection is a process that chooses a subset of features from the original fingerprint features so that the feature space is optimally reduced according to a certain criterion. In this study, a Genetic Algorithms (GAs) approach to fingerprint feature selection is proposed and selected features are input to Artificial Neural Networks (ANNs) for fingerprint recognition. The performance has been tested on fingerprint recognition.
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Altun, A.A., Allahverdi, N. (2007). Neural Network Based Recognition by Using Genetic Algorithm for Feature Selection of Enhanced Fingerprints. In: Beliczynski, B., Dzielinski, A., Iwanowski, M., Ribeiro, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2007. Lecture Notes in Computer Science, vol 4432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71629-7_53
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DOI: https://doi.org/10.1007/978-3-540-71629-7_53
Publisher Name: Springer, Berlin, Heidelberg
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