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Reseach Article

Classification of Human Ear by Extracting Hog Features and Support Vector Machine

by Naveena M., G. Hemantha Kumar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 40
Year of Publication: 2020
Authors: Naveena M., G. Hemantha Kumar
10.5120/ijca2020919902

Naveena M., G. Hemantha Kumar . Classification of Human Ear by Extracting Hog Features and Support Vector Machine. International Journal of Computer Applications. 177, 40 ( Feb 2020), 46-49. DOI=10.5120/ijca2020919902

@article{ 10.5120/ijca2020919902,
author = { Naveena M., G. Hemantha Kumar },
title = { Classification of Human Ear by Extracting Hog Features and Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2020 },
volume = { 177 },
number = { 40 },
month = { Feb },
year = { 2020 },
issn = { 0975-8887 },
pages = { 46-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number40/31173-2020919902/ },
doi = { 10.5120/ijca2020919902 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:48:18.465956+05:30
%A Naveena M.
%A G. Hemantha Kumar
%T Classification of Human Ear by Extracting Hog Features and Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 40
%P 46-49
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The approaches proposed by eminent researchers have been discussed in the previous sections. Survey on the researches says that, it defines the uniqueness of a given person. And it is possible in the biometric authentication to have collisions between two people who have completely biometric character. Gabor filter can represents the frequency and orientation of similar to those of the human visual System, and they have been found to be particularly appropriate for texture representation and as well as discrimination. There will be a great research on the HOG, it is purely gradient based and captured the object shape information; it can be used to extract the global feature. HOG compute edge gradient of whole image and find orientation of each pixel so it can generate histogram easily. Many of them used the Support vector machine for classification because it avoids the over fitting, and it can built kernel and also it is an approximation to a bound on the test error rate. The theory behind SVM suggests that it should a good idea.

References
  1. Ear Biometrics: A Survey of Detection, Feature Extraction and Recognition Methods A. Pflug, Christoph Busch July 2, 2012
  2. Ear Biometrics in Human Identification System V.K. N.Kumar 1 and B, Srinivasan 21 Assistant Professor,PG & Research Department of Computer Science, Gobi Arts & Science College,Erode District, Tamil Nadu, India.
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  5. Histograms of Oriented Gradients for Human Detection Navneet Dalal and Bill Triggs INRIA Rhône-Alps, 655 avenue de l’Europe, Montbonnot 38334,France.
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  7. A Multimodal SVM Approach for Fused Biometric Recognition Geethu S Kumar Jyothirmati Devi Department of Computer Science and Engineering College of Engineering , Chengannur, Kerala
  8. Comparison of Texture Features Based on Gabor Filters Simona E. Grigorescu, Nicolai Petkov, and Peter Kruizinga.
  9. Gabor wavelet transform and its application Wei-lun Chao R98942073
  10. A Survey on Human Ear Recognition Suvarnsing Bhable1S.Tharewal Dr. K.V.Kale Department of Computer Science and Information Technology Dr. Babasaheb Ambedkar Marathwada University, Aurangabad.
Index Terms

Computer Science
Information Sciences

Keywords

Support Vector Machine HOG