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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Jain, Anil K., Nandakumar Karthik, and Ross Arun. 2016. 50 Years of Biometric Research: Accomplishments, Challenges, and Opportunities. Pattern Recognition Letters 79: 80–105. https://doi.org/10.1016/j.patrec.2015.12.013.
Jain, Anil K., Arun A. Ross, and Karthik, Nandakumar. 2011. Introduction to Biometrics. Berlin: Springer.
Jain, Anil K., Yi Chen, and Meltem, Demirkus. 2007. Level 3 Features for Fingerprint Matching. U.S. Patent Application No. 11/692,524.
Zhao, Qijun, Jianjiang Feng, and Anil K. Jain. 2010. Latent Fingerprint Matching: Utility of Level 3 Features. MSU Technical Report, vol. 8, 1–30.
Gupta, Dipalee, and Siddhartha Choubey. 2015. Discrete Wavelet Transform for Image Processing. International Journal of Emerging Technology and Advanced Engineering 4 (3): 598–602.
Yao, Yuan, Paolo Frasconi, and Massimiliano, Pontil. 2001. Fingerprint classification with combinations of support vector machines. In International Conference on Audio-and Video-Based Biometric Person Authentication. Berlin, Heidelberg: Springer.
Byun, Hyeran, and Seong-Whan Lee. 2002. Applications of Support Vector Machines for Pattern Recognition: A Survey. Pattern Recognition with Support Vector Machines, 213–236. Berlin, Heidelberg: Springer.
Kittler, Josef, et al. 1998. On Combining Classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (3): 226–239.
Verlinde, Patrick, and G. Cholet. 1999. Comparing decision fusion paradigms using k-NN based classifiers, decision trees and logistic regression in a multi-modal identity verification application. In Proceedings International Conference Audio and Video-Based Biometric Person Authentication (AVBPA).
https://in.mathworks.com/matlabcentral/fileexchange/52507-fingerprint-color-image-database-v1.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-15-1480-7_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1479-1
Online ISBN: 978-981-15-1480-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)