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
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: firstname.lastname@example.org, email@example.com).
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).