Next Article in Journal
Comparing Relational and Ontological Triple Stores in Healthcare Domain
Previous Article in Journal
Acknowledgement to Reviewers of Entropy in 2016
Article Menu
Issue 1 (January) cover image

Export Article

Open AccessArticle
Entropy 2017, 19(1), 26; doi:10.3390/e19010026

Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy

1
Department of Computer Technology, University of Alicante, Alicante 03690, Spain
2
Department of Computer Science and Artificial Intelligence, University of Alicante, Alicante 03690, Spain
3
Department of Applied Mathematics, University of Alicante, Alicante 03690, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Raúl Alcaraz Martínez
Received: 14 November 2016 / Revised: 15 December 2016 / Accepted: 22 December 2016 / Published: 11 January 2017
(This article belongs to the Section Information Theory)
View Full-Text   |   Download PDF [5467 KB, uploaded 11 January 2017]   |  

Abstract

Face detection is the first step of any automated face recognition system. One of the most popular approaches to detect faces in color images is using a skin color segmentation scheme, which in many cases needs a proper representation of color spaces to interpret image information. In this paper, we propose a fuzzy system for detecting skin in color images, so that each color tone is assumed to be a fuzzy set. The Red, Green, and Blue (RGB), the Hue, Saturation and Value (HSV), and the YCbCr (where Y is the luminance and Cb,Cr are the chroma components) color systems are used for the development of our fuzzy design. Thus, a fuzzy three-partition entropy approach is used to calculate all of the parameters needed for the fuzzy systems, and then, a face detection method is also developed to validate the segmentation results. The results of the experiments show a correct skin detection rate between 94% and 96% for our fuzzy segmentation methods, with a false positive rate of about 0.5% in all cases. Furthermore, the average correct face detection rate is above 93%, and even when working with heterogeneous backgrounds and different light conditions, it achieves almost 88% correct detections. Thus, our method leads to accurate face detection results with low false positive and false negative rates. View Full-Text
Keywords: skin color segmentation; fuzzy expert systems; entropy; fuzzy c-partition; machine learning skin color segmentation; fuzzy expert systems; entropy; fuzzy c-partition; machine learning
Figures

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Pujol, F.A.; Pujol, M.; Jimeno-Morenilla, A.; Pujol, M.J. Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy. Entropy 2017, 19, 26.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top