Color Invariant Skin Segmentation

A. Lynn Abbott, Xu Han, Abhijit Sarkar

Abstract:

This paper addresses the problem of automatically
detecting human skin in images without reliance on color
information. A primary motivation of the work has been
to achieve results that are consistent across the full range
of skin tones, even while using a training dataset that is
significantly biased toward lighter skin tones. Previous
skin-detection methods have used color cues almost exclusively,
and we present a new approach that performs well
in the absence of such information. A key aspect of
the work is dataset repair through augmentation that is
applied strategically during training, with the goal of color
invariant feature learning to enhance generalization. We
have demonstrated the concept using two architectures, and
experimental results show improvements in both precision
and recall for most Fitzpatrick skin tones in the benchmark
ECU dataset. We further tested the system with the
RFW dataset to show that the proposed method performs
much more consistently across different ethnicities, thereby
reducing the chance of bias based on skin color. To demonstrate
the effectiveness of our work, extensive experiments
were performed on grayscale images as well as images
obtained under unconstrained illumination and with artificial
filters. Source code will be provided with the final
version of this paper