Enhancing model-based skin color detection: From low-level RGB features to high-level discriminative binary-class features

We propose two very effective high-level binary-class features to enhance model-based skin color detection. First we find that the log likelihood ratio of the testing data between skin and non-skin RGB models can be a good discriminative feature. We also find that namely the background-foreground co...

Full description

Saved in:
Bibliographic Details
Published in2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 1401 - 1404
Main Authors You-Chi Cheng, Zhe Feng, Fuliang Weng, Chin-Hui Lee
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2012
Subjects
Adaptation models
Bayesian adaptation
Bayesian methods
Correlation
Discriminative feature
Feature extraction
Image color analysis
Lighting
likelihood ratio
score fusion
Skin
skin color model
Online AccessGet full text

Cover

More Information
Summary:We propose two very effective high-level binary-class features to enhance model-based skin color detection. First we find that the log likelihood ratio of the testing data between skin and non-skin RGB models can be a good discriminative feature. We also find that namely the background-foreground correlation provides another complementary feature compared to the conventional low-level RGB feature. Further improvement can be accomplished by Bayesian model adaptation and feature fusion. By jointly considering both schemes of Bayesian model adaptation and feature fusion, we attain the best system performance. Experimental results show that the proposed joint framework improves the 68% to 84% baseline F 1 scores to as high as almost 90% in a wide range of lighting conditions.
ISBN:1467300454
9781467300452
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2012.6288153