Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009, Article ID 856039, 13 pages doi:10.1155/2009/856039 Research Article On the Performance of Kernel Methods for Skin Color Segmentation A. Guerrero-Curieses, 1 J. L. Rojo- ´ Alvarez, 1 P. Conde-Pardo, 2 I. Landesa-V ´ azquez, 2 J. Ramos-L ´ opez, 1 and J. L. Alba-Castro 2 1 Departamento de Teor´ ıa de la Se˜ nal y Comunicaciones, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain 2 Departamento de Teor´ ıa de la Se˜ nal y Comunicaciones, Universidad de Vigo, 36200 Vigo, Spain Correspondence should be addressed to A. Guerrero-Curieses, alicia.guerrero@urjc.es Received 26 September 2008; Revised 23 March 2009; Accepted 7 May 2009 Recommended by C.-C. Kuo 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, CIEL ∗ a ∗ b ∗ , 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. Copyright © 2009 A. Guerrero-Curieses et al. 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. 1. Introduction Skin detection is often the first step in many image processing man-machine applications, such as face detection [1, 2], gesture recognition [3], video surveillance [4], human video tracking [5], or adaptive video coding [6]. Although pixelwise skin color alone is not sufficient for segmenting human faces or hands, color segmentation for skin detection has been proven to be an effective preprocessing step for the subsequent processing analysis. The segmentation task in most of the skin detection literature is achieved by using simple thresholding [7], histogram analysis [8], single Gaussian distribution models [9], or Gaussian Mixture Models (GMM) [1, 10, 11]. The main drawbacks of the distribution-based parametric modeling techniques are, first, their strong dependence on the chosen color space and lighting conditions, and second, the need for selection of the appropriate model for statistical characterization of both the skin and the nonskin classes [12]. Even with an accurate estimation of the parameters in any density-based parametric models, the best detection rate in skin color segmentation cannot be ensured. When a nonparametric modeling is adopted instead, a relatively high number of samples is required for an accurate representation of skin and nonskin regions, like histograms [13] or Neural Networks (NN) [12]. Recently, the suitability of kernel methods has been pointed out as an alternative approach for skin segmentation in color spaces [14–17]. First, the Support Vector Machine (SVM) was proposed for classifying pixels into skin or nonskin samples, by stating the segmentation problem as a binary classification task [17], and later, some authors have proposed that the main interest in skin segmentation could be an adequate description of the domain that supports the skin pixels in the space color, rather than devoting effort to model the more heterogeneous nonskin