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 Pencocokan Citra Sidik Jari Menggunakan Korelasi Silang Ternormalisasi

2015 ◽  
Vol 11 (4) ◽  
pp. 144
Author(s):  
Bulkis Kanata
Keyword(s):  
Image Matching ◽  
Cross Correlation ◽  
Region Of Interest ◽  
Fingerprint Recognition ◽  
Fingerprint Matching ◽  
Fingerprint Image ◽  
Image Capture ◽  
Matching Algorithm ◽  
Specific Part ◽  
Finger Pressure

<p>Fingerprint image matching is an important procedure in fingerprint recognition. Robust fingerprint image matching under a variety of different image capture conditions is difficult to achieve, because of changes in finger pressure, variation of the angle, etc. Fingerprint matching is very important for the development of fingerprint system recognition that is sensitive to finger pressure. This paper proposes a fingerprint matching algorithm that enables the so-called fingerprint template (extracted specific part (region of interest (ROI)) of a person’s fingerprints to be matched to the different fingerprint of the same person or different people taken on different time, angle and a different finger pressure using normalized cross-correlation (NCC). This algorithm was implemented in MATLAB. The results showed that the maximum NCC value for ROI of the source fingerprints and targets that was greater than 0.62 indicates a strong correlation or similarity.</p><p> </p>

Research of Automatic Fingerprint Recognition Essential Algorithms

2011 ◽  
Vol 135-136 ◽  
pp. 739-742
Author(s):  
Jin Hai Zhang
Keyword(s):  
Identity Authentication ◽  
Fingerprint Recognition ◽  
Theoretical Research ◽  
Identification System ◽  
Fingerprint Matching ◽  
Significant Progress ◽  
Fingerprint Image ◽  
Matching Algorithm ◽  
Minutiae Extraction ◽  
Extraction Algorithm

Fingerprint recognition has wide application prospect in all fields which contain identity authentication. Construction of accurate and reliable，safe and Practical automatic fingerprint identification system(AFIS) has become researc hotspot. Although theoretical research and application developmen of AFIS have got a significant Progress，accuracy of the algorithm and proeessing speeds till need to be improved. In this paper, fingerprint image preprocessing algorithms，fingerprint singular Points and minutiae extraction algorithm and fingerprint matching algorithm are analyzed and discussed in detail.

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.

scholarly journals A Novel 2D Feature Extraction Method for Fingerprints Using Minutiae Points and Their Intersections

2017 ◽  
Vol 7 (5) ◽  
pp. 2547
Author(s):  
Nibras Ar Rakib ◽  
SM Zamshed Farhan ◽  
Md Mashrur Bari Sobhan ◽  
Jia Uddin ◽  
Arafat Habib
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Fingerprint Recognition ◽  
Support Vector ◽  
Svm Classifier ◽  
Fingerprint Image ◽  
Feature Extraction Method ◽  
Proposed Model ◽  
Recognition Systems ◽  
To Come

The field of biometrics has evolved tremendously for over the last century. Yet scientists are still continuing to come up with precise and efficient algorithms to facilitate automatic fingerprint recognition systems. Like other applications, an efficient feature extraction method plays an important role in fingerprint based recognition systems. This paper proposes a novel feature extraction method using minutiae points of a fingerprint image and their intersections. In this method, initially, it calculates the ridge ends and ridge bifurcations of each fingerprint image. And then, it estimates the minutiae points for the intersection of each ridge end and ridge bifurcation. In the experimental evaluation, we tested the extracted features of our proposed model using a support vector machine (SVM) classifier and experimental results show that the proposed method can accurately classify different fingerprint images.

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 Matching Algorithm Based on Fuzzy Similarity

2012 ◽  
Vol 433-440 ◽  
pp. 3495-3499
Author(s):  
Gui Liang Zhu ◽  
Rui Fang Song ◽  
Xiao Qiang Zhang ◽  
Xue Hui Fan
Keyword(s):  
Bifurcation Point ◽  
Turning Point ◽  
Fingerprint Matching ◽  
High Complexity ◽  
Fingerprint Image ◽  
Matching Algorithm ◽  
Ridge Line ◽  
Fuzzy Similarity ◽  
Environment Factor ◽  
Low Efficiency

For the low efficiency and high complexity of previous fingerprint matching algorithms, a fingerprint matching algorithm based on the fuzzy close-degree is proposed. It takes the turning point, bifurcation point, the number of ridge line between two points in the fingerprint image and so on as the feature of the object. The new algorithm can improve the accuracy of fingerprint matching. Finally, we point out that the performance of this new algorithm is slightly influenced by the environment factor in pratice, such as the shift and rotation of the fingerprint.

scholarly journals Decision Fusion based Person Identification System using Fingerprints and Facial Image with Template Matching

2018 ◽  
Vol 6 (3) ◽  
Author(s):  
A. K. M. Akhtar Hossain
Keyword(s):  
Face Recognition ◽  
Response Time ◽  
Template Matching ◽  
Recognition System ◽  
Decision Fusion ◽  
Fingerprint Recognition ◽  
Identification System ◽  
Prototype System ◽  
Recognition Systems ◽  
Fusion Scheme

In this research, it has been developed a prototype biometric system which integrates facial images and fingerprints. The system overcomes the limitations of face recognition systems as well as fingerprint recognition systems. The integrated prototype system operates in the identification mode with an admissible response time. The identity established by the system is more reliable than the identity established by a face recognition system. In addition, the proposed decision fusion scheme enables performance improvement by integrating multiple features with different confidence measures. Experimental results demonstrate that the system performs  well. It meet up the response time as well as the accuracy requirements.

Fingerprint Liveness Detection Using Multiple Static Features and Random Forests

2014 ◽  
Vol 14 (04) ◽  
pp. 1450021
Author(s):  
Yanyan Guo ◽  
Xiangdong Fei ◽  
Qijun Zhao
Keyword(s):  
Fingerprint Recognition ◽  
Classification Error ◽  
Fingerprint Image ◽  
Classification Error Rate ◽  
First Order ◽  
Liveness Detection ◽  
Cepstral Analysis ◽  
Detection Approach ◽  
Fingerprint Liveness Detection ◽  
Recognition Systems

It has been demonstrated that fingerprint recognition systems are susceptible to spoofing by presenting a well-duplicated synthetic such as a gummy finger. This paper proposes a novel software-based liveness detection approach using multiple static features. Given a fingerprint image, the static features, including fingerprint coarseness, first-order statistics and intensity-based features, are extracted. Unlike previous methods, the fingerprint coarseness is modeled as multiplicative noise rather than additive noise and is extracted by cepstral analysis. A random forest classifier is employed to select effective features among the extracted features and to differentiate fake from live fingerprints. The proposed method has been evaluated on the standard database provided in the Fingerprint Liveness Detection Competition 2009 (LivDet2009). Compared with other state-of-the-art methods, the proposed method reduces the average classification error rate by more than 20%.

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