On the performance of kernel methods for skin color segmentation

  • Authors:

  • A. Guerrero-Curieses;J. L. Rojo-Álvarez;P. Conde-Pardo;I. Landesa-Vázquez;J. Ramos-López;J. L. Alba-Castro

  • Affiliations:

  • Departamento de Teoría de la Señal y Comunicaciones, Universidad Rey Juan Carlos, Fuenlabrada, Spain;Departamento de Teoría de la Señal y Comunicaciones, Universidad Rey Juan Carlos, Fuenlabrada, Spain;Departamento de Teoría de la Señal y Comunicaciones, Universidad de Vigo, Vigo, Spain;Departamento de Teoría de la Señal y Comunicaciones, Universidad de Vigo, Vigo, Spain;Departamento de Teoría de la Señal y Comunicaciones, Universidad Rey Juan Carlos, Fuenlabrada, Spain;Departamento de Teoría de la Señal y Comunicaciones, Universidad de Vigo, Vigo, Spain

  • Venue:
  • EURASIP Journal on Advances in Signal Processing
  • Year:
  • 2009

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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.