Fingerprint classification based on statistical features and singular point information

Authors:
Zhi Han;Chang-Ping Liu
Affiliations:
Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China
Venue:
IWBRS'05 Proceedings of the 2005 international conference on Advances in Biometric Person Authentication
Year:
2005

Citing 7
Cited 1

Fingerprint pattern classification

Pattern Recognition
Analyzing oriented patterns

Computer Vision, Graphics, and Image Processing
Fingerprints classification using artificial neural networks: a combined structural and statistical approach

Neural Networks
Fingerprint classification and matching using a filterbank

Fingerprint classification and matching using a filterbank
Fingerprint classification using fast Fourier transform and nonlinear discriminant analysis

Pattern Recognition
Type Classification of Fingerprints: A Syntactic Approach

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint classification using a feedback-based line detector

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

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

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automatic fingerprint classification is an effective means to increase the matching speed of an Automatic Fingerprint Identification System with a large-scale fingerprint database. In this paper, an automatic fingerprint classification method is proposed to classify the fingerprint image into one of five classes: Arch, Left loop, Right Loop, Whorl and Tented Arch. First, the information of core points, which is detected with a two-stage method, is applied to determine the reference point in fingerprint image. Then three different features based on statistical properties of small image blocks, which are likely to degrade with image quality deterioration, are calculated from the region of interest and form a 300-dimension feature vector. The feature vector is inputted into a three-layer Back Propagation Network (BPN) classifier and a 5-dimension vector is outputted, each dimension of which corresponds to one of 5 fingerprint classes. Finally, the fingerprints are classified with integrate analysis of the BPN classifier output and singular point information. The accuracy of 93.23% with no rejection is achieved on NIST-4 database and experimental results show that the proposed method is feasible and reliable for fingerprint classification.