Open Access

On the Performance of Kernel Methods for Skin Color Segmentation

  • A. Guerrero-Curieses1Email author,
  • J. L. Rojo-Álvarez1,
  • P. Conde-Pardo2,
  • I. Landesa-Vázquez2,
  • J. Ramos-López1 and
  • J. L. Alba-Castro2
EURASIP Journal on Advances in Signal Processing20092009:856039

Received: 26 September 2008

Accepted: 7 May 2009

Published: 14 June 2009


Human skin detection in color images is a key preprocessing stage in many image processing applications. Though kernel-based methods have been recently pointed out as advantageous for this setting, there is still few evidence on their actual superiority. Specifically, binary Support Vector Classifier (two-class SVM) and one-class Novelty Detection (SVND) have been only tested in some example images or in limited databases. We hypothesize that comparative performance evaluation on a representative application-oriented database will allow us to determine whether proposed kernel methods exhibit significant better performance than conventional skin segmentation methods. Two image databases were acquired for a webcam-based face recognition application, under controlled and uncontrolled lighting and background conditions. Three different chromaticity spaces (YCbCr, , and normalized RGB) were used to compare kernel methods (two-class SVM, SVND) with conventional algorithms (Gaussian Mixture Models and Neural Networks). Our results show that two-class SVM outperforms conventional classifiers and also one-class SVM (SVND) detectors, specially for uncontrolled lighting conditions, with an acceptably low complexity.

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Authors’ Affiliations

Departamento de Teoría de la Señal y Comunicaciones, Universidad Rey Juan Carlos
Departamento de Teoría de la Señal y Comunicaciones, Universidad de Vigo


© A. Guerrero-Curieses et al. 2009

This article is published under license to BioMed Central Ltd. 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.