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SVM-based synthetic fingerprint discrimination algorithm and quantitative optimization strategy
- Source :
- PLoS ONE, Vol 9, Iss 10, p e111099 (2014), PLoS ONE
- Publication Year :
- 2014
- Publisher :
- Public Library of Science (PLoS), 2014.
-
Abstract
- Synthetic fingerprints are a potential threat to automatic fingerprint identification systems (AFISs). In this paper, we propose an algorithm to discriminate synthetic fingerprints from real ones. First, four typical characteristic factors-the ridge distance features, global gray features, frequency feature and Harris Corner feature-are extracted. Then, a support vector machine (SVM) is used to distinguish synthetic fingerprints from real fingerprints. The experiments demonstrate that this method can achieve a recognition accuracy rate of over 98% for two discrete synthetic fingerprint databases as well as a mixed database. Furthermore, a performance factor that can evaluate the SVM's accuracy and efficiency is presented, and a quantitative optimization strategy is established for the first time. After the optimization of our synthetic fingerprint discrimination task, the polynomial kernel with a training sample proportion of 5% is the optimized value when the minimum accuracy requirement is 95%. The radial basis function (RBF) kernel with a training sample proportion of 15% is a more suitable choice when the minimum accuracy requirement is 98%.
- Subjects :
- Computer and Information Sciences
Support Vector Machine
Imaging Techniques
Computer science
Computer Vision
Image Processing
Value (computer science)
lcsh:Medicine
Image Analysis
Object Recognition (Image Processing)
Research and Analysis Methods
Machine Learning
Machine Learning Algorithms
Artificial Intelligence
Fingerprint
Polynomial kernel
Support Vector Machines
Feature (machine learning)
Humans
Radial basis function
Dermatoglyphics
lcsh:Science
Multidisciplinary
lcsh:R
Biology and Life Sciences
Models, Theoretical
Sample (graphics)
Target Detection
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Kernel (statistics)
Signal Processing
Cognitive Science
Engineering and Technology
lcsh:Q
Algorithm
Algorithms
Research Article
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 9
- Issue :
- 10
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....6992c5b89ba0ddb408c4c5073f536316