Abstract
This work uses supervised machine learning algorithms to validate the performance of fingerprint recognition. We extracted fingerprint ridge contours, a level 3 feature, from low-resolution fingerprint images that are used for fingerprint recognition. The fingerprint classification is done using support vector machine and logistic regression classifiers. The database of color images taken from the Internet is split into training and test datasets. First, the images are enhanced using Gabor filter and wavelet. The ridge contours are extracted using canny edge detection filter. We create feature vectors from the extracted ridge contours. These features are used for the fingerprint recognition. The performance of the fingerprint recognition system is evaluated and analyzed using different machine learning algorithms. The Python 2.7 programming language using OpenCV, sklearn, Pickle, NumPy packages for image processing is used in this work.
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Acknowledgements
We would like to show our gratitude to Vishwakarma Institute Of Information Technology, Pune for showing their pearls of wisdom with us during the proposed work. Also thanks to ‘Copyright (c) 2015, Ujwalla Gawande, Kamal Hajari and Yogesh Golhar, All rights reserved’, for the fingerprint database.
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Velapure, A., Talware, R. (2020). Performance Analysis of Fingerprint Recognition Using Machine Learning Algorithms. In: Raju, K., Govardhan, A., Rani, B., Sridevi, R., Murty, M. (eds) Proceedings of the Third International Conference on Computational Intelligence and Informatics . Advances in Intelligent Systems and Computing, vol 1090. Springer, Singapore. https://doi.org/10.1007/978-981-15-1480-7_19
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DOI: https://doi.org/10.1007/978-981-15-1480-7_19
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