A Contactless Fingerprint Verification Method using a Minutiae Matching Technique

2016 ◽  
Vol 6 (1) ◽  
pp. 12-27 ◽  
Author(s):  
Tahirou Djara ◽  
Marc Kokou Assogba ◽  
Antoine Vianou
Keyword(s):  
Orientation Angle ◽  
Input Image ◽  
Fingerprint Matching ◽  
Matching Method ◽  
Fingerprint Verification ◽  
Excessive Pressure ◽  
Verification Method ◽  
Matching Technique ◽  
Euclidean Distances ◽  
Minutiae Matching

Most of matching or verification phases of fingerprint systems use minutiae types and orientation angle to find matched minutiae pairs from the input and template fingerprints. Unfortunately, due to some non-linear distortions, like excessive pressure and fingers twisting during enrollment, this process can cause the minutiae features to be distorted from the original. The authors are then interested in a fingerprint matching method using contactless images for fingerprint verification. After features extraction, they compute Euclidean distances between template minutiae (bifurcation and ending points) and input image minutiae. They compute then after bifurcation ridges orientation angles and ending point orientations. In the decision stage, they analyze the similarity between templates. The proposed algorithm has been tested on a set of 420 fingerprint images. The verification accuracy is found to be acceptable and the experimental results are promising.

A Secured Contactless Fingerprint Verification Method Using a Minutiae Matching Technique

2018 ◽  
pp. 221-245
Author(s):  
Tahirou Djara ◽  
Marc Kokou Assogba ◽  
Antoine Vianou
Keyword(s):  
Orientation Angle ◽  
Input Image ◽  
Fingerprint Matching ◽  
Fingerprint Verification ◽  
Excessive Pressure ◽  
Verification Method ◽  
Template Protection ◽  
Euclidean Distances ◽  
Minutiae Matching ◽  
Future Work

Most matching or verification phases of fingerprint systems use minutiae types and orientation angle to find matched minutiae pairs from the input and template fingerprints. Unfortunately, due to some non-linear distortions, like excessive pressure and fingers twisting during enrollment, this process can cause the minutiae features to be distorted from the original. The authors are interested in a fingerprint matching method using contactless images for fingerprint verification. After features extraction, they compute Euclidean distances between template minutiae (bifurcation and ending points) and input image minutiae. They compute then after bifurcation ridges orientation angles and ending point orientations. In the decision stage, they analyze the similarity between templates. The proposed algorithm has been tested on a set of 420 fingerprint images. The verification accuracy is found to be acceptable and the experimental results are promising. Future work will enhance the proposed verification method by a new template protection technique.

scholarly journals FINGERPRINT VERIFICATION BY USING MINUTIAE MATCHING TECHNIQUE

2020 ◽  
Vol 2 (10) ◽  
Author(s):  
BAPPA SARKAR ◽  
JOYASSREE SEN ◽  
MD. ATIQUR RAHMAN ◽  
MD. HABIBUR RAHMAN
Keyword(s):  
Fingerprint Verification ◽  
Matching Technique ◽  
Minutiae Matching

scholarly journals Cross-Sensor Fingerprint Matching Method Based on Orientation, Gradient, and Gabor-HoG Descriptors With Score Level Fusion

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 28951-28968 ◽  
Author(s):  
Helala Alshehri ◽  
Muhammad Hussain ◽  
Hatim A. Aboalsamh ◽  
Mansour A. Al Zuair
Keyword(s):  
Fingerprint Matching ◽  
Matching Method ◽  
Score Level Fusion ◽  
Orientation Gradient ◽  
Level Fusion

HISTOGRAM OF TRANSITION FOR HUMAN HEAD RECOGNITION

2016 ◽  
Vol 78 (5-9) ◽  
Author(s):  
Panca Mudjirahardjo ◽  
M. Fauzan Edy Purnomo ◽  
Rini Nur Hasanah ◽  
Hadi Suyono
Keyword(s):  
Feature Extraction ◽  
Color Image ◽  
Recognition Rate ◽  
Orientation Angle ◽  
Human Head ◽  
Input Image ◽  
Foreground Extraction ◽  
Multi Scale ◽  
Novel Method ◽  
Main Component

The main component for head recognition is a feature extraction. One of them as our novel method is histogram of transition. This feature is relied on foreground extraction. In this paper we evaluate some pre-processing to get foreground extraction before we calculate the histogram of transition.We evaluate the performance of recognition rate in related with preprocessing of input image, such as color, size and orientation. We evaluate for Red-Green-Blue (RGB) and Hue-saturation-Value (HSV) color image; multi scale of 10×15 pixels, 20×30 pixels and 40×60 pixels; and multi orientation angle of 315o, 330o, 345o, 15o, 30o, and 45o.For comparison, we compare the recognition rate with the existing method of feature extraction, i.e. Histogram of Oriented Gradients (HOG) and Linear Binary Pattern (LBP). The experimental results show Histogram of Transition robust for changing of color, size and orientation angle.

scholarly journals Comparison of Various Frequency Matching Schemes for Geometric Correction of Geostationary Ocean Color Imager

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5564
Author(s):  
Jong-Hwan Son ◽  
Han-Gyeol Kim ◽  
Hee-Jeong Han ◽  
Taejung Kim
Keyword(s):  
Frequency Domain ◽  
Ocean Color ◽  
Phase Correlation ◽  
Range Limits ◽  
Geometric Correction ◽  
Matching Method ◽  
Search Range ◽  
Frequency Matching ◽  
Matching Technique ◽  
Matching Schemes

Current precise geometric correction of Geostationary Ocean Color Imager (GOCI) image slots is performed by shoreline matching. However, it is troublesome to handle slots with few or no shorelines, or slots covered by clouds. Geometric correction by frequency matching has been proposed to handle these slots. In this paper, we further extend previous research on frequency matching by comparing the performance of three frequency domain matching methods: phase correlation, gradient correlation, and orientation correlation. We compared the performance of each matching technique in terms of match success rate and geometric accuracy. We concluded that the three frequency domain matching method with peak search range limits was comparable to geometric correction performance with shoreline matching. The proposed method handles translation only, and assumes that rotation has been corrected. We need to do further work on how to handle rotation by frequency matching.

Complete Pattern Matching for DNA Computing

2006 ◽  
Vol 05 (04) ◽  
pp. 337-343
Author(s):  
Nadia Nedjah ◽  
Luiza De Macedo Mourelle
Keyword(s):  
Information Retrieval ◽  
Logic Programming ◽  
Pattern Matching ◽  
Theorem Proving ◽  
Dna Computing ◽  
Term Rewriting ◽  
Matching Method ◽  
Subject Term ◽  
Matching Technique ◽  
Complete Matching

Pattern matching is essential in many applications such as information retrieval, logic programming, theorem-proving, term rewriting and DNA-computing. It usually breaks down into two categories: root and complete pattern matching. Root matching determines whether a subject term is an instance of a pattern in a pattern set while complete matching determines whether a subject term contains a sub-term that is an instance of a pattern in a pattern set. For the sake of efficiency, root pattern matching need to be deterministic and lazy. Furthermore, complete pattern matching also needs to be parallel. Unlike root pattern matching, complete matching received little interest from the researchers of the field. In this paper, we present a novel deterministic multi-threaded complete matching method. This method subsumes a deterministic lazy root matching technique that was developped by the authors in an earlier work. We evaluate the performance of proposed method using theorem-proving and DNA-computing applications.

Probabilistic Model Based Hybrid Classifier for Character Recognition

2017 ◽  
Vol 25 (04) ◽  
pp. 621-647
Author(s):  
A. K. Sampath ◽  
N. Gomathi
Keyword(s):  
Probabilistic Model ◽  
Character Recognition ◽  
Recognition Rate ◽  
Size Variation ◽  
Orientation Angle ◽  
Recognition System ◽  
Input Image ◽  
Hybrid Classifier ◽  
Form Processing ◽  
Model Based

Handwritten character recognition is most crucial one indulging in many of the applications like forensic search, searching historical manuscripts, mail sorting, bank check reading, tax form processing, book and handwritten notes transcription etc. The problem occurrence in the recognition is mainly because of the writing style variation, size variation (length and height), orientation angle etc. In this paper a probabilistic model based hybrid classifier is proposed for the character recognition combining the neural network and decision tree classifiers. In addition to the local gradient features i.e. histogram oriented feature and grid level feature, an additional feature called GLCM feature is extracted from the input image in the proposed recognition system and are concatenated for the image recognition procedure to encode color, shape, texture, local as well as the statistical information. These extracted features considered are given to the hybrid classifier which recognises the character. In the test set, recognition accuracy of 95% is achieved. The proposed probabilistic model based hybrid classifier tends to contribute more accurate character recognition rate compared to the existing character recognition system.

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