Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/61875
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Title: Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy
Authors: Pujol, Francisco A. | Pujol, Mar | Jimeno-Morenilla, Antonio | Pujol López, María José
Research Group/s: UniCAD: Grupo de investigación en CAD/CAM/CAE de la Universidad de Alicante | Informática Industrial e Inteligencia Artificial
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial | Universidad de Alicante. Departamento de Matemática Aplicada
Keywords: Skin color segmentation | Fuzzy expert systems | Entropy | Fuzzy c-partition | Machine learning
Knowledge Area: Arquitectura y Tecnología de Computadores | Ciencia de la Computación e Inteligencia Artificial | Matemática Aplicada
Issue Date: 11-Jan-2017
Publisher: MDPI
Citation: Pujol FA, Pujol M, Jimeno-Morenilla A, Pujol MJ. Face Detection Based on Skin Color Segmentation Using Fuzzy Entropy. Entropy. 2017; 19(1):26. doi:10.3390/e19010026
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.
Sponsor: This work has been supported by the Ministerio de Economía y Competitividad (Spain), Project TIN2013-40982-R. Project co-financed with FEDER funds.
URI: http://hdl.handle.net/10045/61875
ISSN: 1099-4300
DOI: 10.3390/e19010026
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2017 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
Peer Review: si
Publisher version: http://dx.doi.org/10.3390/e19010026
Appears in Collections:INV - UNICAD - Artículos de Revistas
INV - i3a - Artículos de Revistas

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