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 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>

Multi-Template Processing of Fingerprint Recognition Algorithm

2014 ◽  
Vol 610 ◽  
pp. 332-338
Author(s):  
Lian Ying Zou ◽  
Ying Zhou ◽  
Xiang Dong ◽  
Yu Chen
Keyword(s):  
Image Processing ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Ridge Line ◽  
Black And White ◽  
White Point ◽  
Direction Filter ◽  
Fingerprint Feature ◽  
Line Texture

Using multi-template processing algorithm, the fingerprint features are accurately collected. Through normalization, make the black and white point contrast of the fingerprint image more obviously, strengthen the ridge line texture. Direction calculating algorithm is based on the grey value of the neighborhood pixels. It can be implemented simply and speedily. Through direction filter, noises can be removed, and the contrast of the fingerprint’s ridge lines and valley lines can be enhanced. After binary converting, all information of the fingerprint is stored with 0 and 1. The effect of thinning is to make the fingerprint image more distinct to extract the fingerprint feature point easily. These steps had been implemented on Altera DE2 board with HDL codes. The experimental results indicate that the multi-template algorithm of fingerprint image processing is correct and practicable.

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

Performance Analysis of Fingerprint Based Image Enhancement and Minutiae Extraction

2018 ◽  
Vol 8 (5) ◽  
pp. 43
Author(s):  
Rajneesh Jain ◽  
Sheelesh Kr. Sharma ◽  
Pankaj Agrawal
Keyword(s):  
Performance Analysis ◽  
Image Enhancement ◽  
Histogram Equalization ◽  
Fingerprint Identification ◽  
Fingerprint Image ◽  
Minutiae Extraction ◽  
Fingerprint Pattern ◽  
Fingerprint Image Enhancement

Extracting minutiae from fingerprint images is one of the most important steps in automatic fingerprint identification and classification. Minutiae are local discontinuities in the fingerprint pattern, mainly terminations and bifurcations. In this work we have propose a method for fingerprint image enhancement. Using histogram equalization over filtering and then minutia are calculated. The   results   achieved   are compared   with   those   obtained   through   some   other methods.  The Results show some improvement in the minutiae extraction in terms of quantity.

Preprocessing and Feature Extraction, Coding, Matching Algorithm for Fingerprint Image

2013 ◽  
Vol 805-806 ◽  
pp. 1900-1906
Author(s):  
He Ping Jia
Keyword(s):  
Feature Extraction ◽  
Gabor Filter ◽  
Recognition Rate ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Development Platform ◽  
Traditional Algorithm ◽  
Fingerprint Image Enhancement ◽  
Global And Local

A set of fingerprint recognition algorithm was achieved mainly including Gamma controller normalization and equalizing, fingerprint image division, fingerprint image binarization and different direction Gabor filter for feature extraction; especially Fingerprint image enhancement and the textures based on Gabor filter, taking account of both global and local features of the fingerprints.using matlab 7.0 for development platform was verified,The experimental results showed the proposed algorithm can avoid all sorts of false characters more effectively and recognition rate is higher than traditional algorithm in the same conditions.

scholarly journals Enhancing Fingerprint Image Recognition Algorithm Using Fractional Derivative Filters

2017 ◽  
Vol 7 (1) ◽  
pp. 9-16 ◽  
Author(s):  
Hossein Baloochian ◽  
Hamid Reza Ghaffary ◽  
Saeed Balochian
Keyword(s):  
Fractional Derivative ◽  
Fractional Derivatives ◽  
Gabor Filter ◽  
Input Image ◽  
Recognition Algorithm ◽  
Fingerprint Recognition ◽  
Fingerprint Image ◽  
Direction Estimation ◽  
First Time

Abstract One of the most important steps in recognizing fingerprint is accurate feature extraction of the input image. To enhance the accuracy of fingerprint recognition, an algorithm using fractional derivatives is proposed in this paper. The proposed algorithm uses the definitions of fractional derivatives Riemann-Liouville (R-L) and Grunwald-Letnikov (G-L) in two sections of direction estimation and image enhancement for the first time. Based on it, new mask of fractional derivative Gabor filter is calculated. The proposed fractional derivative-based method enhances the image quality. This method enhances the structure of ridges and grooves of fingerprint, using fractional derivatives. The efficiency of the proposed method is studied in images of FVC2004 (DB1, DB2, DB3 and DB4) database and the results are evaluated using the criteria including entropy, average gradient, and edge intensity. Also, performance of the proposed method is compared with other technical methods such as Gabor filter. Based on the obtained results from the tests, the method is able to enhance the quality of fingerprint images significantly.

scholarly journals 2-D IMPLEMENTATION OF DIGITAL GABOR FILTER DESIGN USING VERILOG

2013 ◽  
pp. 52-56
Author(s):  
K. J. SUSHANTH ◽  
N. SHANKARAIAH
Keyword(s):  
Filter Design ◽  
Gabor Filter ◽  
Fingerprint Recognition ◽  
Verilog Hdl ◽  
Fingerprint Verification ◽  
Fingerprint Image ◽  
Design Synthesis ◽  
Area Efficiency ◽  
Specific Frequency ◽  
Verification Process

Fingerprint or Face Image enhancement using Gabor filter is one of highly computational complexity in fingerprint verification process. Gabor filter has a complex valued convolution kernel and a data format with complex values is used. So implementing Gabor filter is very significant in fingerprint verification process. Designing Gabor filter will help enhancing the quality of fingerprint image. In fingerprint recognition, Gabor filter optimally capture both local orientation and frequency information from a fingerprint image. By tuning a Gabor filter to specific frequency and direction, the local frequency and orientation information can be obtained. Thus, it is suited for extracting texture information from images. This paper presents the implementation of 2-D Gabor Filter design using Verilog HDL. This paper details important enhancement made to the 2D -Digital Gabor filter to minimize the sizing problem and the coding style that synthesizable. The intention is to study, analyze, simplify and improvise the design synthesis efficiency and accuracy while maintaining the same functionality. The result provides area efficiency architecture for the effective design.

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>

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 Image Processor and RISC MCU Embedded Single Chip Fingerprint Sensor

2020 ◽  
Vol 9 (4) ◽  
pp. 51
Author(s):  
Seungmin Jung
Keyword(s):  
Processing Time ◽  
Gabor Filter ◽  
Complementary Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Single Chip ◽  
Fingerprint Image ◽  
Chip Area ◽  
Gate Count ◽  
Pixel Array ◽  
Fingerprint Sensor

In this paper, we propose a single chip fingerprint sensor with the algorithm processor and 16-bit MCU. The algorithm processor is a logic circuit that implements the GABOR filter and the THINNING step, which occupies 80% of the fingerprint image processing time. The rest of the algorithm is processed by embedded 16-bit MCU with small circuit volume, so all steps of the algorithm can be processed on the single chip without an external CPU. The capacitive sensing circuit was designed by applying the parasitic-insensitive integrator with the variable clock generator. The function was verified by Cadence Spectre for a 1-pixel sensor scheme and RTL and post simulation for digital blocks synthesized by Synopsys Design Compiler in 180n 2-poly 6-metal CMOS (complementary metal–oxide–semiconductor) process. The layout is done by automatic P&R for the full chip in a 96 × 96 pixel array. The chip area is 5010 μm × 5710 μm (28.61 mm2) and the gate count is 2,866,700. The result is compared with a conventional one. The proposed scheme can reduce the processing time by 57%.

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