Abstract
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.
Highlights
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]
We introduce the notation and briefly review the segmentation algorithms used in the context of skin segmentation applications, namely, the well-known Gaussian Mixture Models (GMM) segmentation and the kernel methods with binary Support Vector Machine (SVM) and one-class Support Vector Novelty Detection (SVND) algorithms
We analyzed the performance of two conventional skin detectors (GMM and MLP), and three kernel methods
Summary
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]. 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. Pixelwise skin detection in color still images is usually accomplished in three steps: (i) color space transformation, (ii) parametric or nonparametric color distribution modeling, and (iii) binary skin/nonskin decision. The first step in skin segmentation, color space transformation, has been widely acknowledged as a necessary stage to deal with the perceptual nonparametricuniformity and with the high correlation among RGB channels, due to their mixing of luminance and chrominance information. The selection of an adequate color space is largely dependent on factors like the robustness to changing illumination spectra, the selection of a suitable distribution model, and the memory or complexity constraints of the running application
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