Table of Content

Open Access iconOpen Access

ARTICLE

Gender Recognition Based on Computer Vision System

Li-Hong Juanga, Ming-Ni Wub, Shin-An Linb

a School of Electrical Engineering and Automation, Xiamen University of Technology, No.600, Ligong Road, Jimei, Xiamen, 360124, p. R.China;
b Department of Information management, national taichung university of technology, taichung, taiwan RoC

* Corresponding Author: li-Hong Juang, email

Intelligent Automation & Soft Computing 2018, 24(2), 249-256. https://doi.org/10.1080/10798587.2016.1272777

Abstract

Detecting human gender from complex background, illumination variations and objects under computer vision system is very difficult but important for an adaptive information service. In this paper, a preliminary design and some experimental results of gender recognition will be presented from the walking movement that utilizes the gait-energy image (GEI) with denoised energy image (DEI) pre-processing as a machine learning support vector machine (SVM) classifier to train and extract its characteristics. The results show that the proposed method can adopt some characteristic values and the accuracy can reach up to 100% gender recognition rate under combining the horizontal added vertical feature and using a normal image size and test data when people are walking at a fixed angle. Meanwhile, it will be able to achieve over 80% rate within some allowed fault-tolerant angle range.

Keywords


Cite This Article

APA Style
Juang, L., Wu, M., Lin, S. (2018). Gender recognition based on computer vision system. Intelligent Automation & Soft Computing, 24(2), 249-256. https://doi.org/10.1080/10798587.2016.1272777
Vancouver Style
Juang L, Wu M, Lin S. Gender recognition based on computer vision system. Intell Automat Soft Comput . 2018;24(2):249-256 https://doi.org/10.1080/10798587.2016.1272777
IEEE Style
L. Juang, M. Wu, and S. Lin, “Gender Recognition Based on Computer Vision System,” Intell. Automat. Soft Comput. , vol. 24, no. 2, pp. 249-256, 2018. https://doi.org/10.1080/10798587.2016.1272777



cc Copyright © 2018 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1383

    View

  • 1019

    Download

  • 0

    Like

Share Link