Fingerprint Classification Combining Curvelet Transform and Gray-Level Cooccurrence Matrix
Joint Authors
Dong, Tingting
Luo, Jing
Geng, Shuze
Xiu, Chunbo
Song, Dan
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-17
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Fingerprint classification is an important indexing scheme to reduce fingerprint matching time for a large database for efficient large-scale identification.
The abilities of Curvelet transform capturing directional edges of fingerprint images make the fingerprint suitable to be classified for higher classification accuracy.
This paper presents an efficient algorithm for fingerprint classification combining Curvelet transform (CT) and gray-level cooccurrence matrix (GLCM).
Firstly, we use fast discrete Curvelet transform warping (FDCT_WARPING) to decompose the original image into five scales Curvelet coefficients and construct the Curvelet filter by Curvelet coefficients relationship at adjacent scales to remove the noise from signals.
Secondly, we compute the GLCMs of Curvelet coefficients at the coarsest scale and calculate 16 texture features based on 4 GLCMs.
Thirdly, we construct 49 direction features of Curvelet coefficients at the other four scales.
Finally, fingerprint classification is accomplished by K-nearest neighbor classifiers.
Extensive experiments were performed on 4000 images in the NIST-4 database.
The proposed algorithm achieves the classification accuracy of 94.6 percent for the five-class classification problem and 96.8 percent for the four-class classification problem with 1.8 percent rejection, respectively.
The experimental results verify that proposed algorithm has higher recognition rate than that of wavelet-based techniques.
American Psychological Association (APA)
Luo, Jing& Song, Dan& Xiu, Chunbo& Geng, Shuze& Dong, Tingting. 2014. Fingerprint Classification Combining Curvelet Transform and Gray-Level Cooccurrence Matrix. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-15.
https://search.emarefa.net/detail/BIM-483482
Modern Language Association (MLA)
Luo, Jing…[et al.]. Fingerprint Classification Combining Curvelet Transform and Gray-Level Cooccurrence Matrix. Mathematical Problems in Engineering No. 2014 (2014), pp.1-15.
https://search.emarefa.net/detail/BIM-483482
American Medical Association (AMA)
Luo, Jing& Song, Dan& Xiu, Chunbo& Geng, Shuze& Dong, Tingting. Fingerprint Classification Combining Curvelet Transform and Gray-Level Cooccurrence Matrix. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-15.
https://search.emarefa.net/detail/BIM-483482
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-483482