Paper
14 March 2005 Skin color constancy for illumination invariant skin segmentation
Author Affiliations +
Proceedings Volume 5685, Image and Video Communications and Processing 2005; (2005) https://doi.org/10.1117/12.587934
Event: Electronic Imaging 2005, 2005, San Jose, California, United States
Abstract
Accuracy of skin segmentation algorithms is highly sensitive to changes in lighting conditions. When the lighting condition in a scene is different from that in the training examples, miss-classification rate of the skin segmentation algorithms is high. Using color constancy approach we aim to compensate for skin color variations to achieve accurate skin color segmentation. Skin color constancy is realized in an unsupervised manner by using the color changes observed on a face for different illuminations to drive the model. By training on few faces of different ethnicities, our model is able to generalize the color mapping for any unseen ethnicity. The color changes observed are used to learn the color mapping from one lighting condition to the other. These mappings are represented in a low dimensional subspace to obtain basis vector fields. Using these basis vector fields we can model the nonlinear color changes to transform skin colors in arbitrary lighting conditions to a reference lighting condition. We show the proof of concept of unsupervised skin color constancy on faces from the PIE database. Skin segmentation is performed on the color compensated faces using a Skin Distribution Map (SDM), which is trained on skin colors in reference lighting condition.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Rajkiran Gottumukkal and Vijayan Asari "Skin color constancy for illumination invariant skin segmentation", Proc. SPIE 5685, Image and Video Communications and Processing 2005, (14 March 2005); https://doi.org/10.1117/12.587934
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Cited by 3 scholarly publications.
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KEYWORDS
Skin

Light sources and illumination

Image segmentation

Databases

Scanning probe microscopy

RGB color model

Light

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