scholarly journals Application of Machine Learning in Fingerprint Image Enhancement and Recognition: A Review

2021 ◽  
pp. 33-37
Author(s):  
Kshitij Singh ◽  
Dr. Gireesh Kumar Dixit
Keyword(s):  
Recognition System ◽  
Fingerprint Recognition ◽  
Automated System ◽  
Biometric Recognition ◽  
Security Systems ◽  
Fingerprint Image ◽  
Comprehensive Review ◽  
Improve Accuracy ◽  
Fingerprint Image Enhancement ◽  
Biometric Feature

Biometric characteristics helps to recognize an individual among others. Each individual has a unique biometric feature. So, an automated system is designed to recognize an individual. In today’s growing AI development, biometric recognition is applied in many security systems. One of oldest and widely used authentic biometric methodology is fingerprint recognition. Many fingerprint recognition algorithms are designed and developed in order to reduce error rate and to improve accuracy. In this paper, a comprehensive review is presented on various techniques used for fingerprint recognition system along with their performance and their limitations. The purpose of this paper is to review various recent work on the fingerprint recognition system, to explain step by step the steps for recognizing fingerprints, and to provide summaries of the fingerprint databases with functionality

Application of Fingerprint-Matching Algorithm in Smart Gun Using Touch-Less Fingerprint Recognition System

2020 ◽  
pp. 122-128
Author(s):  
Saifullah Khalid
Keyword(s):  
Recognition System ◽  
Fingerprint Recognition ◽  
Fingerprint Matching ◽  
Fingerprint Image ◽  
Matching Algorithm ◽  
Latent Fingerprint ◽  
Recognition Systems

Fingerprint recognition systems are widely used in the field of biometrics. Many existing fingerprint sensors acquire fingerprint images as the user's fingerprint is contacted on a solid flat sensor. Because of this contact, input images from the same finger can be quite different and there are latent fingerprint issues that can lead to forgery and hygienic problems. For these reasons, a touchless fingerprint recognition system has been investigated, in which a fingerprint image can be captured without contact. While this system can solve the problems which arise through contact of the user's finger, other challenges emerge.

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.

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.

Score-Based Fusion in Multi-Unit Biometric Recognition System

2011 ◽  
Vol 48-49 ◽  
pp. 1010-1013 ◽  
Author(s):  
Yong Li ◽  
Jian Ping Yin ◽  
En Zhu
Keyword(s):  
Recognition System ◽  
Fingerprint Recognition ◽  
Fusion Model ◽  
Biometric Recognition ◽  
Score Level Fusion ◽  
Fusion Rules ◽  
Biometric Systems ◽  
Score Fusion ◽  
Multiple Units ◽  
Level Fusion

The performance of biometric systems can be improved by combining multiple units through score level fusion. In this paper, different fusion rules based on match scores are comparatively studied for multi-unit fingerprint recognition. A novel fusion model for multi-unit system is presented first. Based on this model, we analyze the five common score fusion rules: sum, max, min, median and product. Further, we propose a new method: square. Note that the performance of these strategies can complement each other, we introduce the mixed rule: square-sum. We prove that square-sum rule outperforms square and sum rules. The experimental results show good performance of the proposed methods.

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 Segmentation of Fingerprint Image Based on Gradient Magnitude and Coherence

2015 ◽  
Vol 5 (5) ◽  
pp. 1202
Author(s):  
Saparudin Saparudin ◽  
Ghazali Sulong
Keyword(s):  
Recognition System ◽  
Fingerprint Recognition ◽  
Segmentation Method ◽  
Fingerprint Image ◽  
Low Contrast ◽  
Local Variance ◽  
System A ◽  
Foreground Region ◽  
Processing Step ◽  
Gradient Magnitude

Fingerprint image segmentation is an important pre-processing step in automatic fingerprint recognition system. A well-designed fingerprint segmentation technique can improve the accuracy in collecting clear fingerprint area and mark noise areas. The traditional grey variance segmentation method is widely and easily used, but it can hardly segment fingerprints with low contrast of high noise. To overcome the low image contrast, combining two-block feature; mean of gradient magnitude and coherence, where the fingerprint image is segmented into background, foreground or noisy regions,  has been done. Except for the noisy regions in the foreground, there are still such noises existed in the background whose coherences are low, and are mistakenly assigned as foreground. A novel segmentation method based on combination local mean of grey-scale and local variance of gradient magnitude is presented in this paper. The proposed extraction begins with normalization of the fingerprint. Then, it is<span style="text-decoration: line-through;"> </span>followed by foreground region separation from the background. Finally, the gradient coherence approach is used to detect the noise regions existed in the foreground. Experimental results on NIST-Database14 fingerprint images indicate that the proposed method gives the impressive results.

scholarly journals Improving of Fingerprint Segmentation Images Based on K-MEANS and DBSCAN Clustering

2019 ◽  
Vol 9 (4) ◽  
pp. 2425 ◽  
Author(s):  
El mehdi Cherrat ◽  
Rachid Alaoui ◽  
Hassane Bouzahir
Keyword(s):  
Computing Time ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Fingerprint Image ◽  
Local Variance ◽  
Dbscan Clustering ◽  
Matching Process ◽  
Fingerprint Segmentation

<span lang="EN-US">Nowadays, the fingerprint identification system is the most exploited sector of biometric. Fingerprint image segmentation is considered one of its first processing stage. Thus, this stage affects typically the feature extraction and matching process which leads to fingerprint recognition system with high accuracy. In this paper, three major steps are proposed. First, Soble and TopHat filtering method have been used to improve the quality of the fingerprint images. Then, for each local block in fingerprint image, an accurate separation of the foreground and background region is obtained by K-means clustering for combining 5-dimensional characteristics vector (variance, difference of mean, gradient coherence, ridge direction and energy spectrum). Additionally, in our approach, the local variance thresholding is used to reduce computing time for segmentation. Finally, we are combined to our system DBSCAN clustering which has been performed in order to overcome the drawbacks of K-means classification in fingerprint images segmentation. The proposed algorithm is tested on four different databases. Experimental results demonstrate that our approach is significantly efficacy against some recently published techniques in terms of separation between the ridge and non-ridge region.</span>

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.

Securing Multiple Biometric Data Using SVD and Curvelet-Based Watermarking

2018 ◽  
Vol 12 (4) ◽  
pp. 35-53 ◽  
Author(s):  
Rohit M. Thanki ◽  
Komal Rajendrakumar Borisagar
Keyword(s):  
Speech Signal ◽  
Curvelet Transform ◽  
Image Authentication ◽  
Recognition System ◽  
Singular Value ◽  
Fingerprint Recognition ◽  
Security And Privacy ◽  
Fingerprint Image ◽  
Biometric Data ◽  
Value Decomposition

The security and privacy of biometric data in multibiometric systems has become a hot research topic. In this paper, a singular value decomposition (SVD) and fast discrete curvelet transform (FDCuT)-based watermarking scheme for authenticity of fingerprint image using watermark speech signal has been proposed and analyzed. This scheme also provides security to watermark speech signal, which is inserted into the fingerprint image. This proposed scheme has a number of steps including fingerprint image authentication using watermark speech signal. The human speech signal is taken as secret watermark information and inserting into the human fingerprint image in the proposed scheme. The singular value of high frequency curvelet coefficients of the host fingerprint image is modified according to watermark speech signal to get secured and watermarked fingerprint image. The analysis results show that the performance of fingerprint recognition system is not affected by inserted watermark speech signal into host fingerprint image.

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