Research Article on the Performance of Kernel Methods for Skin Color Segmentation

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. Con de - Pa rdo, 2 I. Landesa-Vazquez,´ 2 J. Ramos-Lopez,´ 1 and J. L. Alba-Castro2 1 Departamento de Teor´ıa de la Senal˜ y Comunicaciones, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain 2 Departamento de Teor´ıa de la Senal˜ y Comunicaciones, Universidad de Vigo, 36200 Vigo, Spain Correspondence should be addressed to A. Guerrero-Curieses, [email protected] 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 both the skin and the nonskin classes [12]. Even with an accurate estimation of the parameters in any density-based Skin detection is often the first step in many image processing parametric models, the best detection rate in skin color man-machine applications, such as face detection [1, 2], segmentation cannot be ensured. When a nonparametric gesture recognition [3], video surveillance [4], human modeling is adopted instead, a relatively high number of video tracking [5], or adaptive video coding [6]. Although samples is required for an accurate representation of skin and pixelwise skin color alone is not sufficient for segmenting nonskin regions, like histograms [13]orNeuralNetworks human faces or hands, color segmentation for skin detection (NN) [12]. hasbeenproventobeaneffective preprocessing step for Recently, the suitability of kernel methods has been the subsequent processing analysis. The segmentation task pointed out as an alternative approach for skin segmentation in most of the skin detection literature is achieved by in color spaces [14–17]. First, the Support Vector Machine using simple thresholding [7], histogram analysis [8], single (SVM) was proposed for classifying pixels into skin or Gaussian distribution models [9], or Gaussian Mixture nonskin samples, by stating the segmentation problem as Models (GMM) [1, 10, 11]. The main drawbacks of the a binary classification task [17], and later, some authors distribution-based parametric modeling techniques are, first, have proposed that the main interest in skin segmentation their strong dependence on the chosen color space and could be an adequate description of the domain that lighting conditions, and second, the need for selection of supports the skin pixels in the space color, rather than the appropriate model for statistical characterization of devoting effort to model the more heterogeneous nonskin 2 EURASIP Journal on Advances in Signal Processing class [14, 15]. According to this hypothesis, one-class kernel several color space transformations have been proposed and algorithms, known in the kernel literature as Support Vector compared [7, 10, 17, 20], none of them can be considered as Novelty Detection (SVND) [18, 19], have been used for skin the optimal one. The selection of an adequate color space is segmentation. largely dependent on factors like the robustness to changing However, and to our best knowledge, few exhaustive per- illumination spectra, the selection of a suitable distribution formance comparison have been made to date for supporting model, and the memory or complexity constraints of the a significant overperformance of kernel methods with respect running application. to conventional skin segmentation algorithms. More, differ- In the last years, experiments over highly representative ent merit figures have been used in different studies, and datasets with uncontrolled lighting conditions have shown even contradictory conclusions have been obtained when that the performance of the detector is degraded by those comparing SVM skin detectors with conventional parametric transformations which drop the luminance component. detectors [16, 17]. Moreover, the advantage of focusing Also, color-distribution modeling has been shown to have on determining the region that supports most of the skin alargereffect on performance than color space selection pixels in SVND algorithms, rather than modeling skin and [7, 21]. As trivially shown in [21], given an invertible one- nonskin regions simultaneously (as done in GMM, NN, to-one transformation between two 3D color spaces, if there and SVM algorithms), has not been thoroughly tested [14, exists an optimum skin detector in one space, there exists 15]. another optimum skin detector that performs exactly the Therefore, we hypothesize that comparative performance same in the transformed space. Therefore, results of skin evaluation on a database, with identical merit figures, will detection reported in literature for different color spaces allow us to determine whether proposed kernel methods must be understood as specific experiments constrained by exhibit significantly better performance than conventional the specific available data, the distribution model chosen skin segmentation methods. For this purpose, two image to fit the specific transformed training data and the train- databases have been acquired for a webcam based face validationtest split to tune the detector. recognition application, under controlled and uncontrolled Jayarametal.[22] showed the performance of 9 lighting and background conditions. Three different chro- color spaces with and without including the luminance maticity spaces (YCbCr, CIEL∗a∗b∗, normalized RGB) are component, on a large set of skin pixels under different used to compare kernel methods (SVM and SVND) with illumination conditions from a face database, and nonskin conventional skin segmentation algorithms (GMM and pixels from a general database. With this experimental NN). setup, histogram-based detection performed consistently The scheme of this paper is as follows. In Section 2, better than Gaussian-based detection, both in 2D and in we summarize the state of the art in skin color repre- 3D spaces, whereas 3D detection performed consistently sentation and segmentation, and we highlight some recent better than 2D detection for histograms but inconsistently findings that explain the apparent lack of consensus on better for Gaussian modeling. Also, regarding color space some issues regarding the optimum color spaces, fitting differences, some transformations performed better than models, and kernel methods. Section 3 summarizes the well- RGB, but the differences were not statistically significant. known GMM formulation, and presents a basic description Phung et al. [12] compared more distribution models of the kernel algorithms that are used here. In Section 4, (histogram-based, Gaussians, and GMM) and decision- performance is evaluated for conventional and for kernel- based classifiers (piecewise linear and NN) over 4 color based segmentations, with emphasis on the free parameters spaces by using their ECU face and skin detection database. tuning. Finally, Section 5 contains the conclusions of our This database is composed of thousands of images with study. indoor and outdoor lighting conditions. The histogram- based Bayes and the MLP classifiers in RGB performed very similarly, and consistently better than the other Gaussian- 2. Background on Color Skin Segmentation based and piecewise linear classifiers. The performance over the four color spaces with high resolution histogram Pixelwise skin detection in color still images is usually modeling was almost the same, as expected. Also, mean accomplished in three steps: (i) color space transformation, performance decreased and variance increased when the (ii) parametric or nonparametric color distribution model- luminance component was discarded. In [17], the perfor- ing, and (iii) binary skin/nonskin decision. We present the mance of nonparametric, semiparametric, and parametric background on the main results in literature that are related approaches was evaluated over sixteen color spaces in 2D to our work in terms of the skin pixels representation and of and 3D, concluding that, in general, the performance does the kernel methods previously

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