scholarly journals User identification system for inked fingerprint pattern based on central moments

2021 ◽  
Vol 24 (2) ◽  
pp. 1149
Author(s):  
Esraa Jaffar Baker ◽  
Sundos Abdulameer Alazawi ◽  
Nada Thanoon Ahmed ◽  
Mohd Arfian Ismail ◽  
Rohayanti Hassan ◽  
...  
Keyword(s):  
Fingerprint Recognition ◽  
Identification System ◽  
Shape Features ◽  
User Identification ◽  
Fingerprint Image ◽  
Central Moments ◽  
Security Applications ◽  
Fingerprint Pattern

The <span>use of the fingerprint recognition has been and remains very important in many security applications and licensing systems. Fingerprint recognition is required in many areas such as licensing access to networks, corporate computers and organizations. In this paper, the system of fingerprint recognition that can be used in several cases of fingerprint such as being rounded at an angle by a randomly inked fingerprint on paper. So, fingerprint image is tooked at a different angle in order to identify the owner of the ink fingerprint. This method involves two working levels. The first one, the fingerprint pattern's shape features are calculated based on the central moments of each image being listed on a regular basis with three states rotation. Each image is rotated at a specified angle. In the second level, the fingerprint holder entered is identified using the previously extracted shape features and compared to the three local databases content of three rotation states. When applied the method for several persons by taken their inked fingerprint on the paper, the accuracy of the system in identifying the owner of the fingerprint after rotation states were close to 83.71.</span>

scholarly journals Dynamic Fingerprint Pattern Lock Mobile Application using Android

2019 ◽  
Vol 8 (2S5) ◽  
pp. 222-225
Keyword(s):  
Mobile Application ◽  
Cell Phone ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Biometric Security ◽  
Fingerprint Pattern ◽  
Numerous Cell ◽  
Authentication System ◽  
The One

In this paper we are examining about information security in mobile. Numerous cell phone creators currently fuse biometric security highlights into their products. Furthermore, some gadget makers presently enable application designers to utilize these highlights through their product advancement packs (SDKs). In this investigation, we use fingerprint recognition with a pattern, to build up a security for mobile application. Before, application had the single time finger press. Here we have utilized various time check and long-term hold confirmation techniques. Inside a constrained time, outline, the unique fingerprint image can be utilized to open the app which has classified information identified with government, banking, training, and so on which must be verified. As the generation of cell phones with fingerprint recognition keeps on expanding, this type of authentication system, the one we present in this paper, will turn into a great safety measure

scholarly journals PENCOCOKAN GAMBAR SIDIK JARI DENGAN KAMERA HANDPHONE MENGGUNAKAN METODE RANSAC DAN TRANSFORMASI AFFINE BERBASIS ANDROID

Compiler ◽  
2017 ◽  
Vol 6 (1) ◽  
Author(s):  
Haruno Sajati ◽  
Dwi Nughraheny ◽  
Nova Adi Suwarso
Keyword(s):  
Image Processing ◽  
Gabor Filter ◽  
Histogram Equalization ◽  
Fingerprint Recognition ◽  
Android Application ◽  
Fingerprint Image ◽  
Android Os ◽  
Support Tools ◽  
Fingerprint Pattern ◽  
Fingerprint Sensor

Fingerprints occur due to stroke differences. These stroke differences have occurred at a time when humans are still fetal form. A normal fingerprint pattern is formed of lines and spaces. These lines are called ridges whereas the spaces between these lines are called valleys. To make an introduction to the fingerprint image requires a variety of support tools. Starting from a fingerprint machine, a smartphone that has a fingerprint sensor and much more. In this research, the acquisition of image is done by grayscaling, histogram equalization, gabor filter, binary, thinning, 8 neighbors, matching.The result of making android application with the method that has been described to show unfavorable results seen from the calculation of the accuracy of 63%. Based on testing the specs android OS devices, this application can run on android with OS 4.4.2 specification kitkat.Fingerprints occur due to stroke differences. These stroke differences have occurred at a time when humans are still fetal form. A normal fingerprint pattern is formed of lines and spaces. These lines are called ridges whereas the spaces between these lines are called valleys. To make an introduction to the fingerprint image requires a variety of support tools. Starting from a fingerprint machine, a smartphone that has a fingerprint sensor and much more. In this research, the acquisition of image is done by grayscaling, histogram equalization, gabor filter, binary, thinning, 8 neighbors, matching.The result of making android application with the method that has been described to show unfavorable results seen from the calculation of the accuracy of 63%. Based on testing the specs android OS devices, this application can run on android with OS 4.4.2 specification kitkat. Keywords : OCR Fingerprint, Fingerprint recognition, Minutiae based matching, Fingerprint image processing.

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>

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.

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.

FINGERPRINT IMAGE ENHANCEMENT USING CNN FILTERING TECHNIQUES

2003 ◽  
Vol 13 (06) ◽  
pp. 453-460 ◽  
Author(s):  
ERTUGRUL SAATCI ◽  
VEDAT TAVSANOGLU
Keyword(s):  
Image Enhancement ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Directional Filter ◽  
Ridge Pattern ◽  
Fingerprint Pattern ◽  
Fingerprint Image Enhancement ◽  
Filtering Techniques

Due to noisy acquisition devices and variation in impression conditions, the ridgelines of fingerprint images are mostly corrupted by various kinds of noise causing cracks, scratches and bridges in the ridges as well as blurs. These cause matching errors in fingerprint recognition. For an effective recognition the correct ridge pattern is essential which requires the enhancement of fingerprint images. Segment by segment analysis of the fingerprint pattern yields various ridge direction and frequencies. By selecting a directional filter with correct filter parameters to match ridge features at each point, we can effectively enhance fingerprint ridges. This paper proposes a fingerprint image enhancement based on CNN Gabor-Type filters.

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 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. .

Sign in / Sign up

Export Citation Format

Share Document