A FUSION METHOD FOR PARTIAL FINGERPRINT RECOGNITION

2013 ◽  
Vol 27 (06) ◽  
pp. 1356009 ◽  
Author(s):  
FANGLIN CHEN ◽  
MING LI ◽  
YI ZHANG
Keyword(s):  
Support Vector Machine ◽  
Fingerprint Recognition ◽  
Experimental Result ◽  
Support Vector ◽  
Fingerprint Matching ◽  
Fusion Method ◽  
Fusion Scheme ◽  
Partial Fingerprint ◽  
Simulation Scheme ◽  
Novel Algorithm

Conventional algorithms for fingerprint recognition are mainly based on minutiae information. However, the small number of minutiae in partial fingerprints is still a challenge in fingerprint matching. In this paper, a novel algorithm is proposed to improve the performance of partial fingerprint matching. A simulation scheme was firstly proposed to construct a serial of partial fingerprints with different area. Then, the influence of the fingerprint area in partial fingerprint recognition is studied. By comparing the performance of partial fingerprint recognition with different fingerprint area, some useful conclusions can be drawn: (1) The decrease of the fingerprint area degrades the performance of partial fingerprint recognition; (2) When the fingerprint area decreases, the genuine matching scores will decrease, whereas the imposter matching scores will increase. Based on these observations, we proposed a fusion scheme based on modified support vector machine (SVM) to combine the area information for fingerprint recognition. Experimental result illustrates the effectiveness of the proposed method.

scholarly journals Partial Fingerprint Recognition Using Support Vector Machine

2010 ◽  
Vol 9 (4) ◽  
pp. 844-848 ◽  
Author(s):  
P. Vijayapras ◽  
Md. N. Sulaiman ◽  
N. Mustapha ◽  
R.W.O.K. Rahmat
Keyword(s):  
Support Vector Machine ◽  
Fingerprint Recognition ◽  
Support Vector ◽  
Partial Fingerprint

Computer Vision Based Orange Grading Using SVM

2013 ◽  
Vol 303-306 ◽  
pp. 1134-1138 ◽  
Author(s):  
Zhi Bin Pan ◽  
Xiao Yan Wei
Keyword(s):  
Computer Vision ◽  
Support Vector Machine ◽  
Experimental Result ◽  
Support Vector ◽  
Color Model ◽  
Fruit Grading ◽  
Hsi Color Model ◽  
Four Levels ◽  
Additional Value

Fruit grading is very important for promoting its additional value. We graded oranges based on its images. Four photos were taken from different view angles for each orange. Both RGB and HSI color model were utilized. We extracted a 28-dimensional feature which can describe the size and color of them. Then support vector machine was used to grade these oranges into four levels. Experimental result shows SVM has promising performance for orange grading.

scholarly journals Optimised genetic algorithm-extreme learning machine approach for automatic COVID-19 detection

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0242899
Author(s):  
Musatafa Abbas Abbood Albadr ◽  
Sabrina Tiun ◽  
Masri Ayob ◽  
Fahad Taha AL-Dhief ◽  
Khairuddin Omar ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Ct Scan ◽  
Extreme Learning Machine ◽  
Experimental Result ◽  
Support Vector ◽  
X Ray ◽  
Chest X Ray ◽  
Learning Machine

The coronavirus disease (COVID-19), is an ongoing global pandemic caused by severe acute respiratory syndrome. Chest Computed Tomography (CT) is an effective method for detecting lung illnesses, including COVID-19. However, the CT scan is expensive and time-consuming. Therefore, this work focus on detecting COVID-19 using chest X-ray images because it is widely available, faster, and cheaper than CT scan. Many machine learning approaches such as Deep Learning, Neural Network, and Support Vector Machine; have used X-ray for detecting the COVID-19. Although the performance of those approaches is acceptable in terms of accuracy, however, they require high computational time and more memory space. Therefore, this work employs an Optimised Genetic Algorithm-Extreme Learning Machine (OGA-ELM) with three selection criteria (i.e., random, K-tournament, and roulette wheel) to detect COVID-19 using X-ray images. The most crucial strength factors of the Extreme Learning Machine (ELM) are: (i) high capability of the ELM in avoiding overfitting; (ii) its usability on binary and multi-type classifiers; and (iii) ELM could work as a kernel-based support vector machine with a structure of a neural network. These advantages make the ELM efficient in achieving an excellent learning performance. ELMs have successfully been applied in many domains, including medical domains such as breast cancer detection, pathological brain detection, and ductal carcinoma in situ detection, but not yet tested on detecting COVID-19. Hence, this work aims to identify the effectiveness of employing OGA-ELM in detecting COVID-19 using chest X-ray images. In order to reduce the dimensionality of a histogram oriented gradient features, we use principal component analysis. The performance of OGA-ELM is evaluated on a benchmark dataset containing 188 chest X-ray images with two classes: a healthy and a COVID-19 infected. The experimental result shows that the OGA-ELM achieves 100.00% accuracy with fast computation time. This demonstrates that OGA-ELM is an efficient method for COVID-19 detecting using chest X-ray images.

scholarly journals Texture classification of fabric defects using machine learning

2020 ◽  
Vol 10 (4) ◽  
pp. 4390
Author(s):  
Yassine Ben Salem ◽  
Mohamed Naceur Abdelkrim
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Comparative Study ◽  
Texture Classification ◽  
Support Vector ◽  
Defect Classification ◽  
Analysis Method ◽  
Fabric Defect ◽  
Novel Algorithm

In this paper, a novel algorithm for automatic fabric defect classification was proposed, based on the combination of a texture analysis method and a support vector machine SVM. Three texture methods were used and compared, GLCM, LBP, and LPQ. They were combined with SVM’s classifier. The system has been tested using TILDA database. A comparative study of the performance and the running time of the three methods was carried out. The obtained results are interesting and show that LBP is the best method for recognition and classification and it proves that the SVM is a suitable classifier for such problems. We demonstrate that some defects are easier to classify than others.

Sign in / Sign up

Export Citation Format

Share Document