On the Performance of Kernel Methods for Skin Color Segmentation. (14th June 2009)

• Record Type: Journal Article
• Authors: Guerrero-Curieses, A.; Rojo-Álvarez, J. L.; Conde-Pardo, P.; Landesa-Vázquez, I.; Ramos-López, J.; Alba-Castro, J. L.
• Other Names: Kuo C.-C. Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2009(2009)
• Publication Date: 2009-06-14
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2009/856039
• Languages: English
• ISSNs: 1687-6172

Abstract

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, 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.