Fingerprint Image Enhancement Algorithm Based on FDCT

2011 ◽  
Vol 255-260 ◽  
pp. 2047-2051 ◽  
Author(s):  
Chong Ben Tao ◽  
Guo Dong Liu
Keyword(s):  
Curvelet Transform ◽  
Input Image ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Threshold Function ◽  
Fingerprint Image ◽  
Fingerprint Enhancement ◽  
Fast Discrete Curvelet Transform ◽  
Fingerprint Image Enhancement ◽  
Enhancement Algorithm

Fingerprint enhancement is an essential preprocessing step and it is crucial for the efficiency of fingerprint recognition algorithm. We present an enhancement algorithm based on fast discrete curvelet transform (FDCT). First, implement positive transform on input image, namely decompose the image into coarse scales and fine scales coefficients. Then make use of a directional filter and a soft threshold function to enhance image and reduce noise respectively. Finally, implement inverse transform, and reconstruct the enhanced image. Experiments are carried out on FVC2004 databases. For bad quality fingerprints, the results indicate that the proposed algorithm has better enhancement and de-noising effect than traditional methods, and need less time.

scholarly journals Influence of Skin Diseases on Fingerprint Recognition

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Martin Drahansky ◽  
Michal Dolezel ◽  
Jaroslav Urbanek ◽  
Eva Brezinova ◽  
Tai-hoon Kim
Keyword(s):  
Image Enhancement ◽  
Strong Influence ◽  
Skin Diseases ◽  
Point Of View ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Fingerprint Image Enhancement ◽  
Electronic Sensors ◽  
Enhancement Algorithm

There are many people who suffer from some of the skin diseases. These diseases have a strong influence on the process of fingerprint recognition. People with fingerprint diseases are unable to use fingerprint scanners, which is discriminating for them, since they are not allowed to use their fingerprints for the authentication purposes. First in this paper the various diseases, which might influence functionality of the fingerprint-based systems, are introduced, mainly from the medical point of view. This overview is followed by some examples of diseased finger fingerprints, acquired both from dactyloscopic card and electronic sensors. At the end of this paper the proposed fingerprint image enhancement algorithm is described.

Preprocessing and Feature Extraction, Coding, Matching Algorithm for Fingerprint Image

2013 ◽  
Vol 805-806 ◽  
pp. 1900-1906
Author(s):  
He Ping Jia
Keyword(s):  
Feature Extraction ◽  
Gabor Filter ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Development Platform ◽  
Traditional Algorithm ◽  
Fingerprint Image Enhancement ◽  
Global And Local

A set of fingerprint recognition algorithm was achieved mainly including Gamma controller normalization and equalizing, fingerprint image division, fingerprint image binarization and different direction Gabor filter for feature extraction; especially Fingerprint image enhancement and the textures based on Gabor filter, taking account of both global and local features of the fingerprints.using matlab 7.0 for development platform was verified,The experimental results showed the proposed algorithm can avoid all sorts of false characters more effectively and recognition rate is higher than traditional algorithm in the same conditions.

scholarly journals Enhancing Fingerprint Image Recognition Algorithm Using Fractional Derivative Filters

2017 ◽  
Vol 7 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Hossein Baloochian ◽  
Hamid Reza Ghaffary ◽  
Saeed Balochian
Keyword(s):  
Fractional Derivative ◽  
Fractional Derivatives ◽  
Gabor Filter ◽  
Input Image ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Direction Estimation ◽  
First Time

Abstract One of the most important steps in recognizing fingerprint is accurate feature extraction of the input image. To enhance the accuracy of fingerprint recognition, an algorithm using fractional derivatives is proposed in this paper. The proposed algorithm uses the definitions of fractional derivatives Riemann-Liouville (R-L) and Grunwald-Letnikov (G-L) in two sections of direction estimation and image enhancement for the first time. Based on it, new mask of fractional derivative Gabor filter is calculated. The proposed fractional derivative-based method enhances the image quality. This method enhances the structure of ridges and grooves of fingerprint, using fractional derivatives. The efficiency of the proposed method is studied in images of FVC2004 (DB1, DB2, DB3 and DB4) database and the results are evaluated using the criteria including entropy, average gradient, and edge intensity. Also, performance of the proposed method is compared with other technical methods such as Gabor filter. Based on the obtained results from the tests, the method is able to enhance the quality of fingerprint images significantly.

Two-Dimensional Sine Filter Fingerprint Enhancement Algorithm Based on Block Level

2011 ◽  
Vol 88-89 ◽  
pp. 596-603
Author(s):  
Xiao Si Zhan ◽  
Ya Yun Chu
Keyword(s):  
Image Enhancement ◽  
Identification System ◽  
Surface Model ◽  
Two Dimensional ◽  
Fingerprint Image ◽  
Fingerprint Enhancement ◽  
Distribution Rule ◽  
Fingerprint Image Enhancement ◽  
Sinusoidal Surface ◽  
Enhancement Algorithm

Enhancing low-quality fingerprint image is the effective method for improving the accuracy of minutia extraction and performance of the automatic fingerprint identification system. Fingerprint image is one kind of regular texture image in nature. To design two-dimensional sinusoidal surface model which accorded with the ridge gray distribution rule and propose the fingerprint image enhancement algorithm based on the two-dimensional sinusoidal surface model after analyzing the basic character of the fingerprint image. The experimental results indicate that the fingerprint image enhancement algorithm has better connecting ability for the broken ridges than the Gabor fingerprint enhancement algorithm. The algorithm can improve effectively the fingerprint image enhancement result and the accuracy of the minutiae extraction.

The VLSI Design of Fingerprint Normalization Algorithm

2010 ◽  
Vol 29-32 ◽  
pp. 2428-2434
Author(s):  
Shi Min Du
Keyword(s):  
Image Quality ◽  
Basic Principle ◽  
Vlsi Design ◽  
Fingerprint Identification ◽  
Fingerprint Image ◽  
Fingerprint Enhancement ◽  
Enhancement Algorithm

Fingerprint identification is one of the most important biometric technologies, which has drawn a substantial amount of attention recently. Fingerprint enhancement algorithm is an important component of fingerprint identification. Normalization is the first step of enhancement algorithm. In this paper, the VLSI design of normalization algorithm is proposed. This paper performs these following tasks. Firstly, the flow of enhancement algorithm and the basic principle of normalization algorithm is introduced. Then, a scheme of VLSI design of normalization algorithm is proposed and described in detail. Finally, we implement this design with Verilog language and present the simulation result with Modesim9.0. The simulation result shows that the scheme proposed in this paper has improved the fingerprint image quality to a certain extent.

Multi-Template Processing of Fingerprint Recognition Algorithm

2014 ◽  
Vol 610 ◽  
pp. 332-338
Author(s):  
Lian Ying Zou ◽  
Ying Zhou ◽  
Xiang Dong ◽  
Yu Chen
Keyword(s):  
Image Processing ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Ridge Line ◽  
Black And White ◽  
White Point ◽  
Direction Filter ◽  
Fingerprint Feature ◽  
Line Texture

Using multi-template processing algorithm, the fingerprint features are accurately collected. Through normalization, make the black and white point contrast of the fingerprint image more obviously, strengthen the ridge line texture. Direction calculating algorithm is based on the grey value of the neighborhood pixels. It can be implemented simply and speedily. Through direction filter, noises can be removed, and the contrast of the fingerprint’s ridge lines and valley lines can be enhanced. After binary converting, all information of the fingerprint is stored with 0 and 1. The effect of thinning is to make the fingerprint image more distinct to extract the fingerprint feature point easily. These steps had been implemented on Altera DE2 board with HDL codes. The experimental results indicate that the multi-template algorithm of fingerprint image processing is correct and practicable.

An improved fingerprint image matching and multi-view fingerprint recognition algorithm

2018 ◽  
Vol 35 (3-4) ◽  
pp. 341-354
Author(s):  
Dewen SENG ◽  
Hanggi ZHANG ◽  
Xujian FANG ◽  
Xuefeng ZHANG ◽  
Jing CHEN
Keyword(s):  
Image Matching ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image
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