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.

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.

A Fingerprint Matching Algorithm Based On Minutiae And Local Ridge Orientation

2016 ◽  
Author(s):  
Marlon Lucas Gomes Salmento ◽  
Fernando Miranda Vieira Xavier ◽  
Bernardo Sotto-Maior Peralva ◽  
Augusto Santiago Cerqueira
Keyword(s):  
Fingerprint Matching ◽  
Matching Algorithm

A turning point-based offline map matching algorithm for urban road networks

2021 ◽  
Vol 565 ◽  
pp. 32-45
Author(s):  
Dongqing Zhang ◽  
Yucheng Dong ◽  
Zhaoxia Guo
Keyword(s):  
Turning Point ◽  
Road Networks ◽  
Map Matching ◽  
Matching Algorithm ◽  
Urban Road

An ultra-low memory fingerprint matching algorithm and its implementation on a 32-bit smart card

2003 ◽  
Vol 49 (2) ◽  
pp. 453-459 ◽  
Author(s):  
Sung Bum Pan ◽  
Daesung Moon ◽  
Younhee Gil ◽  
Dosung Ahn ◽  
Yongwha Chung
Keyword(s):  
Smart Card ◽  
Fingerprint Matching ◽  
Matching Algorithm

A trajectory map matching algorithm via exploring the average directional features of continuous windows

2021 ◽  
pp. 1-16
Author(s):  
Xiaohan Wang ◽  
Pei Wang ◽  
Weilong Chen ◽  
Wangwu Hu ◽  
Long Yang
Keyword(s):  
Road Network ◽  
Transition Probability ◽  
Location Based Services ◽  
Original Position ◽  
Map Matching ◽  
Trajectory Data ◽  
Matching Accuracy ◽  
Data Set ◽  
Matching Algorithm ◽  
Low Efficiency

Many location-based services require a pre-processing step of map matching. Due to the error of the original position data and the complexity of the road network, the matching algorithm will have matching errors when the complex road network is implemented, which is therefore challenging. Aiming at the problems of low matching accuracy and low efficiency of existing algorithms at Y-shaped intersections and roundabouts, this paper proposes a space-time-based continuous window average direction feature trajectory map matching algorithm (STDA-matching). Specifically, the algorithm not only adaptively generates road network topology data, but also obtains more accurate road network relationships. Based on this, the transition probability is calculated by using the average direction feature of the continuous window of the track points to improve the matching accuracy of the algorithm. Secondly, the algorithm simplifies the trajectory by clustering the GPS trajectory data aggregation points to improve the matching efficiency of the algorithm. Finally, we use a real and effective data set to compare the algorithm with the two existing algorithms. Experimental results show that our algorithm is effective.

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