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>

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.

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.

An Improved Rapid SURF Algorithm Based on Region of Interest

2014 ◽  
Vol 945-949 ◽  
pp. 1861-1868
Author(s):  
Ya Jun Liu ◽  
Ren Quan Wan ◽  
Zhong Ren Wang ◽  
Chang Cheng Jiang
Keyword(s):  
Image Matching ◽  
Region Of Interest ◽  
Threshold Value ◽  
Inspection System ◽  
Research Application ◽  
Matching Algorithm ◽  
Vision Inspection ◽  
Traditional Algorithm ◽  
Speed Up ◽  
Speed Up Robust Feature

The purpose of this paper is to research application of speed-up robust feature (SURF) based on the region of interest for workpiece matching and positioning. Thresholding is a simple but important method to perform image segmentation. In order to reduces the complexity of the data and simplifies the process of recognition, the image is segmented by threshold value method which eliminates and suppresses useless information of image background. The image matching algorithm shows a better performance on real-time than the standard SURF and it succeeds in accelerating the speed of image pre-processing before image matching. In addition, the good robustness and adaptability of SURF are maintained. Compared with the traditional algorithm, improved algorithm enhances the efficiency of vision inspection system and can be used in other applications of image matching.

scholarly journals Optical Disc and Blood Vessel Segmentation in Retinal Fundus Images

2019 ◽  
Vol 8 (3) ◽  
pp. 67
Author(s):  
Amira B. Sallow ◽  
Hawkar Kh. Shaikha
Keyword(s):  
Blood Vessel ◽  
Template Matching ◽  
Cross Correlation ◽  
Region Of Interest ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Original Image ◽  
Matching Algorithm ◽  
A New Technique ◽  
Retinal Fundus

Segmentation of optical disk (OD) and blood vessel is one of the significant steps in automatic diabetic retinopathy (DR) detecting. In this paper, a new technique is presented for OD segmentation that depends on the histogram template matching algorithm and OD size. In addition, Kirsch method is used for Blood Vessel (BV) segmentation which is one of the popular methods in the edge detection and image processing technique. The template matching algorithm is used for finding the center of the OD. In this step, the histogram of each RGB (Red, Green, and Blue) planes are founded and then the cross-correlation is founded between the template and the original image, OD location is the point with maximum cross-correlation between them. The OD size varies according to the camera field of sight and the resolution of the original image. The rectangle size of OD is not the same for various databases, the estimated size for DRIVE, STARE, DIARTDB0, and DIARTDB1 are 80×80, 140×140, 190×190, and 190×190 respectively. After finding the OD center and rectangle size of OD, a binary mask is created with Region of Interest (ROI) for segmenting the OD. The DIARTDB0 is used to evaluate the proposed technique, the result is robust and vital with an accuracy of 96%.

An improved fingerprint image matching and multi-view fingerprint recognition algorithm

2018 ◽  
Vol 35 (3-4) ◽  
pp. 341-354
Author(s):  
Dewen SENG ◽  
Hanggi ZHANG ◽  
Xujian FANG ◽  
Xuefeng ZHANG ◽  
Jing CHEN
Keyword(s):  
Image Matching ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image

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.

ROTATED WAVELET FILTERS-BASED FINGERPRINT RECOGNITION

2012 ◽  
Vol 26 (03) ◽  
pp. 1256008 ◽  
Author(s):  
SANGITA D. BHARKAD ◽  
MANESH KOKARE
Keyword(s):  
Acceptance Rate ◽  
Fingerprint Recognition ◽  
Discrete Wavelet ◽  
Fingerprint Matching ◽  
False Acceptance Rate ◽  
Fingerprint Image ◽  
Significant Information ◽  
Wavelet Filters ◽  
Novel Approach

A novel approach for feature extraction of fingerprint matching is proposed by using two-dimensional (2D) rotated wavelet filters (RWF). 2D RWF are used to capture the characterization of diagonally oriented information present in fingerprint image. Proposed method extracts the significant information from small area of fingerprint image. Experimental results conducted on standard database of Bologna University and FVC2002 indicate that the proposed method improves the genuine acceptance rate (GAR) from 92.14% to 96.12% and reduces false acceptance rate (FAR) from 25.2% to 21.2% on Bologna University database and it reduces FAR from 36.71% to 22.79% on FVC2002 database compared with discrete wavelet transform-based approach.

scholarly journals Hough Transform for Distinctive Edge Detection to Images in Fingerprint Recognition Matching Transformation

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 999-1010
Author(s):  
Hayder G.A. Altameemi ◽  
Ahmed Abdul Azeez Ismael ◽  
Raddam Sami Mehsen
Keyword(s):  
Edge Detection ◽  
Hough Transform ◽  
Transformation Method ◽  
Fingerprint Recognition ◽  
Biometric Identification ◽  
Fingerprint Matching ◽  
Matching Algorithm ◽  
Detection Techniques ◽  
Matching Procedure ◽  
Prewitt Edge Detection

Biometric Identification is a globally renowned procedure, which has been utilised to achieve a successful and accurate level of identification. In the sea of biometrics, fingerprints are deemed more popular when it comes to verification. This results from the presence of the ridges on the fingerprints that are completely exclusive to each individual. Besides that, fingerprints are expansively employed to ascertain and authenticate people individually. Therefore, this study had proposed to employ distinctive Edge Detection techniques together with the Hough Transform to match the images of the fingerprints in a fingerprint matching system. The Hough Transform is a superior procedure carried out to get an accurate series of finer points or lines. The finer points or lines would then distinguish the fingerprints. Nevertheless, it was still a challenge to extract finer points or lines from the fingerprints under uninhibited conditions. Therefore, this paper was organised based on four distinctive steps. First, different Edge Detection operators were employed to perform the fingerprint matching algorithm. Next, the fingerprint matching algorithm was applied twice to the same Edge Detection operators. Thirdly, the Edge Detection operators had been substituted with the Transformation Method for the same matching procedure. For example, the proposed fingerprint matching algorithm comprised of the Hough Transform and same Edge Detection operators. Finally, distinct Edge Detection operators based on the decision making algorithm were used to calculate and determine the percentage of matching. Therefore, this study proved that the prints obtained via the Prewitt Edge Detection together with Hough Transform were in an agreement.

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