Image Refination in the Application of Fingerprint Identification

Bo Feng; Li Jiuan Zhang; Hui Tao Zhang

doi:10.4028/www.scientific.net/AMM.155-156.851

Paper Titles

Simulation Analysis of the Anti-Settlement Dehydration of the Phosphate Ester Fire-Resistant Oil Based on Lab VIEW
p.831

Research and Application in Image Processing Technology Based on the Embedded Parallel Computing
p.836

Hail Identification Analysis from Radar Image by CNN
p.841

Simulation of the Cracking Behavior for Fiber Reinforced Composite Materials with the Infulence of Depth-Width Ratio and Direction Angle
p.846

Image Refination in the Application of Fingerprint Identification
p.851

The Control of Humidification and Dew Condensation by Using of On/Off Control of the Bubble Humidifier in the PEMFC
p.856

PCNN Based Welding Seam Image Segmentation Algorithm
p.861

An Adaptive Image Fusion Method Based on NSCT and AGA
p.867

Relationship of Post Treatment Temperature and Tensile Strength for Selective Laser Sintering
p.872

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 155-156Image Refination in the Application of Fingerprint...

Image Refination in the Application of Fingerprint Identification

Abstract:

Aiming at the existing fingerprint algorithm refinement not refined exist completely swallowed, skeleton, lines from lines problems such as center, the paper puts forward the rapid thinning algorithm, through the analysis of target of the direction of the trend lines around, choose to remove or keep connected point around, effectively solve traditional iteration algorithm has refined the problems, and refining the template image square ridge tracking, overcome the gradually to the main lines tracking algorithm are too complicated and not easy to realize. Finally the improved algorithm in FPS200 fingerprint sensor as acquisition device automatic fingerprint identification system for verification. The results show that the algorithm has a good refined effect, get the details of the image further satisfy the requirements of feature extraction.

You might also be interested in these eBooks

Mechanical Engineering and Green Manufacturing II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 155-156)

Pages:

851-855

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.155-156.851

Citation:

Cite this paper

Online since:

February 2012

Authors:

Bo Feng, Li Jiuan Zhang, Hui Tao Zhang

Keywords:

Feature Extraction, Fingerprint, Image Thinning, Minutiae, Pseudo Minutiae

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Andrew K. Hrechak, James A. Mehugh. Automated Fingerprint Recognition Using Structural Matching. Pattern Recognition. (2000).

DOI: 10.1016/0031-3203(90)90134-7

Google Scholar

[2] NALINI K. RATHA, SHAOYN CHEN and ANIL K. JAIN. Adaptive Flow Orientation—Based Feature Extraction InFingerprint Images. Pattern Recognition, (2005).

DOI: 10.1016/0031-3203(95)00039-3

Google Scholar

[3] D.K. Isenor and S.G. ZAKY. Fingerprint Identification UsingGraph Matching. Pattern Recognition. (2006).

Google Scholar

[4] B. Moayer and K.S. Fu. A tree System Approach for Fingerprint. Pattern Recognition, IEEE Trans. Pattern Ana1. Mach. (2006).

DOI: 10.1109/tpami.1986.4767798

Google Scholar

[5] A.P. FITZ and R. J GREEN. Fingerprint Classification UsingA Hekagonal Fast FourierTransform. Pattern Recognition, (1996).

Google Scholar

[6] KALLE KARU and ANIL K. JAIN Fingerprint Classification. Pattern Recognition, (1996).

Google Scholar

[7] MASAHIRO KAWAGOE and AKIO TOJO. Fingerprint Pattern Classification. Pattern Recognition, (2004).

Google Scholar

[8] Federal Bureau of Investigation. Fingerprint Identification. Washington DC：US Government Printing Office, (2007).

Google Scholar