scholarly journals Local Orientation Field Based Nonlocal Means Method for Fingerprint Image De-Noising

2013 ◽  
Vol 04 (03) ◽  
pp. 150-153 ◽  
Author(s):  
J. Zou ◽  
J. B. Feng ◽  
X. M. Zhang ◽  
M. Y. Ding
Keyword(s):  
Local Orientation ◽  
Orientation Field ◽  
Fingerprint Image ◽  
Nonlocal Means

scholarly journals A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection

2020 ◽  
Vol 10 (11) ◽  
pp. 3868
Author(s):  
Jiong Chen ◽  
Heng Zhao ◽  
Zhicheng Cao ◽  
Fei Guo ◽  
Liaojun Pang
Keyword(s):  
Singular Point ◽  
Semantic Segmentation ◽  
Singular Points ◽  
Detection Methods ◽  
Orientation Field ◽  
Global Features ◽  
Fingerprint Image ◽  
Detection Rates ◽  
Fingerprint Classification ◽  
Point Detection

As one of the most important and obvious global features for fingerprints, the singular point plays an essential role in fingerprint registration and fingerprint classification. To date, the singular point detection methods in the literature can be generally divided into two categories: methods based on traditional digital image processing and those on deep learning. Generally speaking, the former requires a high-precision fingerprint orientation field for singular point detection, while the latter just needs the original fingerprint image without preprocessing. Unfortunately, detection rates of these existing methods, either of the two categories above, are still unsatisfactory, especially for the low-quality fingerprint. Therefore, regarding singular point detection as a semantic segmentation of the small singular point area completely and directly, we propose a new customized convolutional neural network called SinNet for segmenting the accurate singular point area, followed by a simple and fast post-processing to locate the singular points quickly. The performance evaluation conducted on the publicly Singular Points Detection Competition 2010 (SPD2010) dataset confirms that the proposed method works best from the perspective of overall indexes. Especially, compared with the state-of-art algorithms, our proposal achieves an increase of 10% in the percentage of correctly detected fingerprints and more than 16% in the core detection rate.

Study of Fingerprint Image Feature Extraction Algorithm

2014 ◽  
Vol 519-520 ◽  
pp. 577-580
Author(s):  
Shuai Yuan ◽  
Guo Yun Zhang ◽  
Jian Hui Wu ◽  
Long Yuan Guo
Keyword(s):  
Feature Extraction ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Image Feature ◽  
Feature Points ◽  
Orientation Field ◽  
Fingerprint Image ◽  
Image Feature Extraction ◽  
Feature Extraction Algorithm ◽  
Extraction Algorithm

Fingerprint image feature extraction is a critical step to fingerprint recognition system, which studies topological structure, mathematical model and extraction algorithm of fingerprint feature. This paper presents system design and realization of feature extraction algorithm for fingerprint image. On the basis of fingerprint skeleton image, feature points including ending points, bifurcation points and singular points are extracted at first. Then false feature points are detected and eliminated by the violent changes of ambient orientation field. True feature points are marked at last. Test result shows that the method presented has good accuracy, quick speed and strong robustness for realtime application.

Research on Fingerprint Image Segmentation

2012 ◽  
Vol 532-533 ◽  
pp. 697-701
Author(s):  
Jun Tao Xue ◽  
Hong Wei Li
Keyword(s):  
Image Segmentation ◽  
Experimental Results ◽  
Statistical Characteristics ◽  
Orientation Field ◽  
Fingerprint Image ◽  
Field Information

As part of fingerprint image pre-processing, fingerprint image segmentation plays an irreplaceable role. This paper proposes a method based on the orientation field information combined with statistical characteristics of gray in order to realize the second segmentation of fingerprint image. Various experimental results show that the method proposed in this paper improves the effect of segmentation.

Analysis of Fingerprint Image Enhancement using Gabor Filtering with Different Orientation Field Values

2017 ◽  
Vol 5 (2) ◽  
pp. 427
Author(s):  
Pakutharivu P ◽  
Srinath M. V
Keyword(s):  
Image Enhancement ◽  
Gabor Filter ◽  
Orientation Angle ◽  
Input Image ◽  
False Identification ◽  
Gabor Filtering ◽  
Orientation Field ◽  
Fingerprint Image ◽  
Frequency Components ◽  
Fingerprint Image Enhancement

<p>Fingerprint image enhancement is the key process in IAFIS systems.  In order to reduce false identification ratio and to supply good fingerprint images to IAFIS systems for exact identification, fingerprint images are generally enhanced.  A filtering process tries to filter out the noise from the input image, and emphasize on low, high and directional spatial frequency components of an image.  This paper presents an experimental summary of enhancing fingerprint images using Gabor filters.  Frequency, width and window domain filter ranges are fixed. The orientation angle alone is modified by 0 radians, ,   and  radians. The experimental results show that Gabor filter enhances the fingerprint image in a better way than other filtering methods and extracts features. </p>

A WAVELET-BASED APPROACH TO RIDGE THINNING IN FINGERPRINT IMAGES

2005 ◽  
Vol 19 (05) ◽  
pp. 631-645 ◽  
Author(s):  
XINGE YOU ◽  
BIN FANG ◽  
YUAN YAN TANG ◽  
ZHENYU HE
Keyword(s):  
Poor Quality ◽  
Automatic Recognition ◽  
Wavelet Function ◽  
Fingerprint Recognition ◽  
Global Feature ◽  
Orientation Field ◽  
Fingerprint Image ◽  
Thinning Algorithm ◽  
Multi Scale ◽  
Effectiveness And Efficiency

As a global feature of fingerprints, the thinning of ridges, extraction of minutiae and computation of orientation field are very important for automatic fingerprint recognition. Many algorithms have been proposed for their computation and estimation, but their results are unsatisfactory, especially for poor quality fingerprint images. In this paper, a robust wavelet-based method to create thinned ridge map of fingerprint for automatic recognition is proposed. Properties of modulus minima based on the spline wavelet function are substantially investigated. Desirable characteristics show that this method is suitable to describe the skeleton of the ridge of the fingerprint image. A multi-scale thinning algorithm based on the modulus minima of wavelet transform is presented. The proposed algorithm is able to improve the skeleton representation of the ridge of the fingerprint without side-effects and limitations of the existing methods. The thinned ridge map can facilitate the extraction of the minutiae for matching in fingerprint recognition. Experiments have been conducted to validate the effectiveness and efficiency of the proposed method.

scholarly journals Entropy-Based Clustering Algorithm for Fingerprint Singular Point Detection

Entropy ◽  
2019 ◽  
Vol 21 (8) ◽  
pp. 786
Author(s):  
Ngoc Tuyen Le ◽  
Duc Huy Le ◽  
Jing-Wein Wang ◽  
Chih-Chiang Wang
Keyword(s):  
Clustering Algorithm ◽  
Adaptive Method ◽  
Orientation Field ◽  
Fingerprint Image ◽  
Enhancement Method ◽  
Field Estimation ◽  
Verification Systems ◽  
Basic Features ◽  
Value Decomposition ◽  
Point Detection

Fingerprints have long been used in automated fingerprint identification or verification systems. Singular points (SPs), namely the core and delta point, are the basic features widely used for fingerprint registration, orientation field estimation, and fingerprint classification. In this study, we propose an adaptive method to detect SPs in a fingerprint image. The algorithm consists of three stages. First, an innovative enhancement method based on singular value decomposition is applied to remove the background of the fingerprint image. Second, a blurring detection and boundary segmentation algorithm based on the innovative image enhancement is proposed to detect the region of impression. Finally, an adaptive method based on wavelet extrema and the Henry system for core point detection is proposed. Experiments conducted using the FVC2002 DB1 and DB2 databases prove that our method can detect SPs reliably.

Fingerprint Image Compression using Fast Fractal ALgorithm

2009 ◽  
Vol 4 (1) ◽  
pp. 52-58
Author(s):  
Karuna Kumar B ◽  
K. Satya Prasad
Keyword(s):  
Image Compression ◽  
Fingerprint Image
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