Gender Recognition using Hog with Maximized Inter-Class Difference

M. E. Yildirim, O. F. Ince, Y. B. Salman, J. Kwan Song, J. Sik Park, B. Woo Yoon

2016

Abstract

Several methods and features have been proposed for gender recognition problem. Histogram of oriented gradients (Hog) is a widely used feature in image processing. This study proposes a gender recognition method using full body features. Human body from side and front view were represented by Hog. Using all bins in the histogram requires longer time for training. In order to decrease the computation time, descriptor size should be decreased. Inter-class difference was obtained as a vector and sorted in a descending order. The bins with the largest value were selected among this vector. Random forest and Adaboost methods were used for the recognition. As a result of both tests, the classifier using first 100 bins with maximum difference gives the optimum performance in terms of accuracy rate and computation time. Although Adaboost performed faster, the accuracy of random forest is higher in full body gender recognition.

Paper Citation

in Harvard Style

Yildirim M., Ince O., Salman Y., Song J., Park J. and Yoon B. (2016). Gender Recognition using Hog with Maximized Inter-Class Difference . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 106-109. DOI: 10.5220/0005715401060109

in Bibtex Style

@conference{visapp16,
author={M. E. Yildirim and O. F. Ince and Y. B. Salman and J. Kwan Song and J. Sik Park and B. Woo Yoon},
title={Gender Recognition using Hog with Maximized Inter-Class Difference},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={106-109},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005715401060109},
isbn={978-989-758-175-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Gender Recognition using Hog with Maximized Inter-Class Difference
SN - 978-989-758-175-5
AU - Yildirim M.
AU - Ince O.
AU - Salman Y.
AU - Song J.
AU - Park J.
AU - Yoon B.
PY - 2016
SP - 106
EP - 109
DO - 10.5220/0005715401060109