Combining singular points and orientation image information for fingerprint classification

Authors:
Jun Li;Wei-Yun Yau;Han Wang
Affiliations:
School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore;Institute for Infocomm Research, Singapore 119613, Singapore;School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore
Venue:
Pattern Recognition
Year:
2008

Citing 14
Cited 9

Fingerprint pattern classification

Pattern Recognition
A Real-Time Matching System for Large Fingerprint Databases

IEEE Transactions on Pattern Analysis and Machine Intelligence
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 Classification by Combination of Flat and Structural Approaches

AVBPA '01 Proceedings of the Third International Conference on Audio- and Video-Based Biometric Person Authentication
Fingerprint Preselection Using Eigenfeatures

CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Localization of corresponding points in fingerprints by complex filtering

Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
A two-stage fingerprint classification system

WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
Fast Robust Fingerprint Feature Extraction and Classification

Journal of Intelligent and Robotic Systems
Constrained nonlinear models of fingerprint orientations with prediction

Pattern Recognition
Handbook of Fingerprint Recognition

Handbook of Fingerprint Recognition
Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis

Pattern Recognition
A graph matching based approach to fingerprint classification using directional variance

AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Fingerprint classification using a feedback-based line detector

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Artificial Neural Network Based Automatic Face Model Generation System from Only One Fingerprint

ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Fingerprint classification based on Adaboost learning from singularity features

Pattern Recognition
Fingerprint Classification Based on Analysis of Singularities and Image Quality

IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
An intelligent automatic face contour prediction system

Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Fingerprint classification based on subclass analysis using multiple templates of support vector machines

Intelligent Data Analysis
A fingerprint retrieval system based on level-1 and level-2 features

Expert Systems with Applications: An International Journal
Fingerprint reference point detection for image retrieval based on symmetry and variation

Pattern Recognition
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

Quantified Score

Hi-index	0.01

Visualization

Abstract

Fingerprint classification is crucial to reduce the processing time in a large-scale database. Two popular features used are the singularities and orientation information and they are complementary. Therefore, an algorithm based on the interactive validation of singular points and the constrained nonlinear orientation model is proposed. The final features used for classification comprises the coefficients of the orientation model and the singularity information. This resulted in very compact feature vector which is used as input to an SVM classifier to perform the classification. The experiments conducted on the NIST database 4 show the effectiveness of the proposed method in producing good classification result.