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On the Performance of Kernel Methods for Skin Color Segmentation
EURASIP Journal on Advances in Signal Processing volume 2009, Article number: 856039 (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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Guerrero-Curieses, A., Rojo-Álvarez, J.L., Conde-Pardo, P. et al. On the Performance of Kernel Methods for Skin Color Segmentation. EURASIP J. Adv. Signal Process. 2009, 856039 (2009). https://doi.org/10.1155/2009/856039
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