Fingerprint Matching Using Transformation Parameter Clustering
IEEE Computational Science & Engineering
Automatic Fingerprint Recognition Systems
Automatic Fingerprint Recognition Systems
Guide to Biometrics
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
ROCR: visualizing classifier performance in R
Bioinformatics
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Support Vector Machines (SVM) project feature vectors into a linear or non-linear state space using kernel function(s) and attempts to maximize the margin between classes. The projection of feature vectors into a high dimensional hyperspace structure helps to provide sparse separable clusters of data. Biometric data such as fingerprints transformed into feature vectors are candidates for support vector machine sparse classification. Fingerprint position and ridge flow pattern classification provide a feature vector for a kernel function(s). As samples are projected into a hyperspace construct, fingerprint identification velocity will improve while performance will increase or remain constant. This paper discusses and shows the results of a fingerprint identification support vector machine within a novel hyperspace structure. The support vector machine is formalized with a high dimensional hyperspace structure with an internal bootstrapped c-means clustering algorithm and probabilistic neural network (PNN). The National Institute of Standards and Technology (NIST) provided data set Special Database 14 for all experiments.