A feedback-based algorithm for motion analysis with application to object tracking
EURASIP Journal on Applied Signal Processing
Fingerprint classification method based on least square support vector machine and detailed image
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Diagnosis of Dental Deformities in Cephalometry Images Using Support Vector Machine
Journal of Medical Systems
Fingerprint classification based on statistical features and singular point information
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
A fingerprint retrieval system based on level-1 and level-2 features
Expert Systems with Applications: An International Journal
Minutia handedness: A novel global feature for minutiae-based fingerprint matching
Pattern Recognition Letters
Fingerprint classification by a hierarchical classifier
Pattern Recognition
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We present a fingerprint classification algorithm in this paper. This algorithm classifies a fingerprint image into one of the five classes: arch, left loop, right loop, whorl, and tented arch. We use a new low-dimensional feature vector obtained from the output of a novel oriented line detector. Our line detector is a co-operative dynamical system that gives oriented lines and preserves multiple orientations at points where differently oriented lines meet. Our feature extraction process is based on characterizing the distribution of orientations around the fingerprint. We discuss three different classifiers: support vector machines, nearest-neighbor classifier, and neural network classifier. We present results obtained on a National Institute of Standards and Technology (NIST) fingerprint database and compare with other published results on NIST databases. All our classifiers perform equally well, and this suggests that our novel line detection and feature extraction process indeed captures all the crucial information needed for classification in this problem.