Detecting skin in face recognition systems: A colour spaces study

  • Authors:

  • Jose M. Chaves-González;Miguel A. Vega-Rodríguez;Juan A. Gómez-Pulido;Juan M. Sánchez-Pérez

  • Affiliations:

  • Univ. Extremadura, Dept. Technologies of Computers and Communications, Escuela Politécnica, Campus Universitario s/n, 10071, Cáceres, Spain;Univ. Extremadura, Dept. Technologies of Computers and Communications, Escuela Politécnica, Campus Universitario s/n, 10071, Cáceres, Spain;Univ. Extremadura, Dept. Technologies of Computers and Communications, Escuela Politécnica, Campus Universitario s/n, 10071, Cáceres, Spain;Univ. Extremadura, Dept. Technologies of Computers and Communications, Escuela Politécnica, Campus Universitario s/n, 10071, Cáceres, Spain

  • Venue:
  • Digital Signal Processing
  • Year:
  • 2010

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Abstract

Skin colour detection is a technique very used in most of face detectors to find faces in images or videos. However, there is not a common opinion about which colour space is the best choice to do this task. Therefore, the motivation for our study is to discover which colour model is the best option to build an efficient face detector which can be embedded in a functional face recognition system. We have studied 10 of the most common and used colour spaces doing different comparisons among them, in order to know which one is the best option for human skin colour detection. In concrete, we have studied the models: RGB, CMY, YUV, YIQ, YPbPr, YCbCr, YCgCr, YDbDr, HSV-or HSI-and CIE-XYZ. To make the comparison among them, we have used 15 truth images where the skin colour of a face is clearly separated from the rest of the image (background, eyes, lips, hair, etc.). Thus we can compare at level pixel each colour model, doing a detailed study of each format. We present the final conclusions comparing different results, such as: right detections, false positives and false negatives for each colour space. According to the obtained results, the most appropriate colour spaces for skin colour detection are HSV model (the winner in our study), and the models YCgCr and YDbDr.