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>

scholarly journals An Increasing Performance of Fingerprint Image Segmentation Based on Clustering and Algorithms

2019 ◽  
Vol 8 (12) ◽  
pp. 4572-4577
Keyword(s):  
Computing Time ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Background Region ◽  
Fingerprint Image ◽  
Local Variance ◽  
Dbscan Clustering ◽  
Matching Process ◽  
Local Block

The fingerprint identification system is nowadays the biometric sector that is most exploited. Segmentation of the fingerprint image is considered as one of its first stage of processing.This stage thus typically affects the extraction and matching process of the feature, resulting in a high accuracy fingerprint recognition system.Three important steps are proposed in this paper. First, to improve the quality of the fingerprint images, Soble and TopHat filtering method were used.K-means clustering for combining 5-dimensional vector characteristics (variance, mean difference, gradient coherence, ridge direction, and energy spectrum) then accurately separates the foreground and background region for each local block in a fingerprint image.Also, local variance thresholding is used in our approach to reducing computing time for segmentation. Finally, we are combined with our DBSCAN clustering system that was performed to overcome the disadvantages of classifying K-means in the segmentation of fingerprint images.In four different databases, the proposed algorithm is tested. Experimental results show that our approach is significantly effective in the separation between the ridge and non-ridge region against some recently published techniques.

scholarly journals An effective and robust fingerprint enhancement by adaptive filtering in frequency domain

2009 ◽  
Vol 22 (1) ◽  
pp. 91-104 ◽  
Author(s):  
Andjelija Raicevic ◽  
Brankica Popovic
Keyword(s):  
Feature Extraction ◽  
Frequency Domain ◽  
Adaptive Filtering ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Fingerprint Image ◽  
Fingerprint Enhancement ◽  
Directional Filters

Extensive research of automatic fingerprint identification system (AFIS), although started in the early 1960s, has not yet give the answer to reliable fingerprint recognition problem. A critical step for AFIS accuracy is reliable extraction of features (mostly minutiae) from the input fingerprint image. However, the effectiveness of a feature extraction relies heavily on the quality of the input fingerprint images. This leads to the incorporation of a fingerprint enhancement module in fingerprint recognition system to make the system robust with respect to the quality of input fingerprint images. In this paper we propose an adaptive filtering in frequency domain in order to enhance fingerprint image. Two different directional filters are proposed and results are compared. .

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 A Gradient Based Approach for Fingerprint Image Segmentation using Morphological Operators

2018 ◽  
Vol 7 (4) ◽  
pp. 2453
Author(s):  
Reji Joy ◽  
Hemalatha S
Keyword(s):  
Image Segmentation ◽  
Individual Recognition ◽  
Input Image ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Morphological Operations ◽  
Fingerprint Image ◽  
Segmentation Process ◽  
Gradient Based ◽  
Fingerprint Segmentation

The advancement of science and technology has made the reliable individual recognition and identification systems to become very popular. From the various biometric characteristics, fingerprint is one of the popular method because of its easiness and not much effort is required to acquire fingerprint. First step for an Automated Fingerprint Identification System (AFIS) is the segmentation of fingerprint from the acquired image. During fingerprint segmentation process the input image is decomposed into foreground and background areas. The foreground area contains information that are needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false features. So in an AFIS, fingerprint image segmentation plays an important role in carefully separating ridge like part (foreground) from noisy background. Gradient based method is commonly used for segmentation process. Since gradient estimation is erroneous in noisy images, the study proposes a combination of gradient mask and morphological operations to segment fingerprint foreground effectively. The results obtained prove that the new method is suited for fingerprint segmentation.

The Research of Algorithms for Fingerprint Characteristic Extraction and Matching

2012 ◽  
Vol 433-440 ◽  
pp. 3479-3482
Author(s):  
Zhen Zhang ◽  
Li Liu
Keyword(s):  
High Efficiency ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Fingerprint Image ◽  
Minutiae Extraction ◽  
Characteristic Extraction ◽  
System A ◽  
Fingerprint Minutiae ◽  
Minutiae Matching

Fingerprint recognition plays an important role in identification of organism characters. Automatic fingerprint identification system(AFIS)is a technology based on computer or microprocessor with advantages of convenience and high efficiency. The extraction and matching of fingerprint minutiae is a necessary step in automatic fingerprint recognition system. A set of algorithms for minutiae extraction and minutiae matching of fingerprint image are proposed in this paper based on the analysis of the inherent minutiae of fingerprint.

scholarly journals A Critical Study on Fingerprint Image Sensing and Acquisition Technology

2017 ◽  
pp. 86-92
Author(s):  
Krishna Prasad K ◽  
P. S. Aithal
Keyword(s):  
New Technologies ◽  
Digital Camera ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Critical Study ◽  
Identification System ◽  
Fingerprint Matching ◽  
Virtual Camera ◽  
Image Sensing

Automatic Fingerprint Recognition System (AFIS) mainly depends on the quality of the fingerprint captured during the enrollment process, even though a lot of techniques developed in literature for fingerprint matching, all most all system is influenced or affected by the quality of acquisition method. Automated fingerprint identification system requires fingerprint images in a special format. Normally it can't receive and process the photographic image or photo taken from virtual camera or cell camera. There are many special acquisition or sensing strategies to extract the ridge-and-valley structure of finger skin or fingerprint. Traditionally, in law or regulation enforcement packages, fingerprints were especially received offline. Fingerprint acquisition can be specially classified into groups as an offline and live scan. An offline acquisition technique gets input through inked affect of the fingertip on paper and digitized with the aid of the paper with an optical scanner or video digital camera. The live acquisition is received through the sensor that is having the ability to directly digitize the sensing tip of the finger. As the fingerprint sensing, image processing, signal processing, and communication technology advance, an increasing number of new technologies in this acquisition technology are arriving at the main facet. In this paper, we discuss different types of fingerprint acquisition technologies, which involve optical, ultrasonic, capacitance, passive capacitance, and active capacitance. This paper helps to identify new fingerprint acquisition technology.

scholarly journals Spectral Minutiae Fingerprint Recognition System

2014 ◽  
pp. 103-108
Author(s):  
S. Shanawaz Basha ◽  
N. Musrat Sultana
Keyword(s):  
Large Scale ◽  
Reduction Rate ◽  
Principal Component ◽  
Rejection Rate ◽  
Recognition System ◽  
Feature Reduction ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Behavioral Characteristics ◽  
Template Size

Biometrics refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics, such as faces, finger prints, iris, and gait. In this paper, we focus on the application of finger print recognition system. The spectral minutiae fingerprint recognition is a method to represent a minutiae set as a fixedlength feature vector, which is invariant to translation, and in which rotation and scaling become translations, so that they can be easily compensated for. Based on the spectral minutiae features, this paper introduces two feature reduction algorithms: the Column Principal Component Analysis and the Line Discrete Fourier Transform feature reductions, which can efficiently compress the template size with a reduction rate of 94%.With reduced features, we can also achieve a fast minutiae-based matching algorithm. This paper presents the performance of the spectral minutiae fingerprint recognition system, this fast operation renders our system suitable for a large-scale fingerprint identification system, thus significantly reducing the time to perform matching, especially in systems like, police patrolling, airports etc,. The spectral minutiae representation system tends to significantly reduce the false acceptance rate with a marginal increase in the false rejection rate.

scholarly journals A multimodal biometric identification system based on cascade advanced of fingerprint, fingervein and face images

2020 ◽  
Vol 17 (3) ◽  
pp. 1562
Author(s):  
El mehdi Cherrat ◽  
Rachid Alaoui ◽  
Hassane Bouzahir
Keyword(s):  
Gabor Filter ◽  
Regression Line ◽  
Histogram Equalization ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Fusion Algorithm ◽  
Decision Level ◽  
Face Images ◽  
Level Method

<p>In this paper, we present a multimodal biometric recognition system that combines fingerprint, fingervein and face images based on cascade advanced and decision level fusion. First, in fingerprint recognition system, the images are enhanced using gabor filter, binarized and passed to thinning method. Then, the minutiae points are extracted to identify that an individual is genuine or impostor. In fingervein recognition system, image processing is required using Linear Regression Line, Canny and local histogram equalization technique to improve better the quality of images. Next, the features are obtained using Histogram of Oriented Gradient (HOG). Moreover, the Convolutional Neural Networks (CNN) and the Local Binary Pattern (LBP) are applied to detect and extract the features of the face images, respectively. In addition, we proposed three different modes in our work. At the first, the person is identified when the recognition system of one single biometric modality is matched. At the second, the fusion is achieved at cascade decision level method based on AND rule when the recognition system of both biometric traits is validated. At the last mode, the fusion is accomplished at decision level method based on AND rule using three types of biometric. The simulation results have demonstrated that the proposed fusion algorithm increases the accuracy to 99,43% than the other system based on unimodal or bimodal characteristics.</p>

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.

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.

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