  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:
https://github.com/HanXuMartin/Color-Invariant-Skin-Segmentation
