Fingerprint pattern classification
Pattern Recognition
Detection of singular points in fingerprint images
Pattern Recognition
A Multichannel Approach to Fingerprint Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Classification by Directional Image Partitioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint sub-classification: a neural network approach
Intelligent biometric techniques in fingerprint and face recognition
Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Robust Fingerprint Feature Extraction and Classification
Journal of Intelligent and Robotic Systems
Fingerprint image segmentation by energy of gaussian-hermite moments
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
IEEE Transactions on Image Processing
Filterbank-based fingerprint matching
IEEE Transactions on Image Processing
Rotation and translation invariants of Gaussian-Hermite moments
Pattern Recognition Letters
Image analysis by Gaussian-Hermite moments
Signal Processing
ATPDI: a computational definition of fingerprint singular points
International Journal of Information Technology and Management
Fast computation of accurate Gaussian-Hermite moments for image processing applications
Digital Signal Processing
A fingerprint retrieval system based on level-1 and level-2 features
Expert Systems with Applications: An International Journal
Fingerprint classification by a hierarchical classifier
Pattern Recognition
Fingerprint classification based on decision tree from singular points and orientation field
Expert Systems with Applications: An International Journal
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The singular points, core and delta, are widely used in fingerprint classification. However a true pair of core and delta that are close to one another is often ignored. In this paper, we define a new type of singular point denoted by S"C"D for representing a pair of core and delta. A new algorithm based on the distribution of Gaussian-Hermite moments is used to detect S"C"D. With core, delta and S"C"D, the accuracy of fingerprint classification is improved, especially for tented arches. The proposed method has been tested on the NIST-4. We can improve the accuracy of algorithm (Zhang and Yan, 2004) [Zhang, Q., Yan, H., 2004. Fingerprint classification based on extraction and analysis of singularities and pseudo ridges. Pattern Recognit. 37, 2233-2243] by 26.7% for identifying tented arch, and the classification accuracy can be improved by 4.3% for five-class problem.