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IJMLC 2020 Vol.10(5): 654-661 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2020.10.5.987

Implementation of HOG Feature Extraction with Tuned Parameters for Human Face Detection

Mohammed G. Mohammed and Amera I. Melhum

Abstract—Extracting and tracking face in image sequences is a required first step in many applications such as face recognition facial expression classification and face tracking, it is a challenging problem in computer vision field because of many factors that effects on the image, some of these factors are luminosity, different face colors, background patterns, face orientation and variability in size, shape, and expression. The objective of this paper is to Experiment wide range of parameters for HOG face detector and setting up the most suitable kernel for Support Vector Machine (SVM) and then, comparing this method with some well-known methods for face detection and identifying the most reliable one. The aim of this study is not providing the best face detector method rather than a try to find out the performance of HOG feature for detecting a face, experimenting different kernels and eventually finding the tuned parameters for HOG descriptors for detecting a face, in this study based on experimental results as shown in Table IV. The HOG + SVM scores the highest value of precision, accuracy, and sensitivity. As 0.8824, 0.9986 and 0.75 respectively compared to Viola-Jones method which scores 0.6512, 0.9973 and 0.7 finally skin color method which scores 0.3968, 0.9947 and 0.625.

Index Terms—Face detection, histogram of oriented gradient, machine learning, support vector machine, viola-jones, skin colour.

The authors are with the Dept. of Computer Science, College of Science, University of Duhok, Duhok, Kurdistan Region, Iraq (e-mail: mohammed.guhdar@uod.ac, amera_melhum@uod.ac).

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Cite: Mohammed G. Mohammed and Amera I. Melhum, "Implementation of HOG Feature Extraction with Tuned Parameters for Human Face Detection," International Journal of Machine Learning and Computing vol. 10, no. 5, pp. 654-661, 2020.

Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net


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