Open AccessPixel‐wise skin colour detection based on flexible neural tree
Author(s) -
Xu Tao,
Wang Yunhong,
Zhang Zhaoxiang
Publication year - 2013
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2012.0657
Subject(s) - ycbcr , artificial intelligence , computer science , rgb color model , pixel , color space , pattern recognition (psychology) , computer vision , particle swarm optimization , image (mathematics) , image processing , color image , machine learning
Skin colour detection plays an important role in image processing and computer vision. Selection of a suitable colour space is one key issue. The question that which colour space is most appropriate for pixel‐wise skin colour detection is not yet concluded. In this study, a pixel‐wise skin colour detection method is proposed based on the flexible neural tree (FNT) without considering the problem of selecting a suitable colour space. A FNT‐based skin model is constructed by using large skin data sets which identifies the important components of colour spaces automatically. Experimental results show improved accuracy and false positive rates (FPRs). The structure and parameters of FNT are optimised via genetic programming and particle swarm optimisation algorithms, respectively. In the experiments, nine FNT skin models are constructed and evaluated on features extracted from RGB, YCbCr, HSV and CIE‐Lab colour spaces. The Compaq and ECU datasets are used for constructing FNT‐based skin model and evaluating its performance compared with other skin detection methods. Without extra processing steps, the authors method achieves state of the art performance in skin pixel classification and better performance in terms of accuracy and FPRs.