This work addresses the problem of automatically detecting human skin in images without reliance on color information.
Unlike previous methods, we present a new approach that performs well in the absence of such information.
A key aspect of the work is that color-space augmentation is applied strategically during the training, with the goal of reducing the influence of features that are based entirely on color and increasing more semantic understanding.
The resulting system exhibits a dramatic improvement in performance for images in which color details are diminished.
We have demonstrated the concept using the U-Net architecture, and experimental results show improvements in evaluations for all Fitzpatrick skin tones in the ECU dataset.
We further tested the system with RFW dataset to show that the proposed method is consistent across different ethnicities and reduces bias to any skin tones.
Therefore, this work has strong potential to aid in mitigating bias in automated systems that can be applied to many applications including surveillance and biometrics. / Master of Science / Skin segmentation deals with the classification of skin and non-skin pixels and regions in a image containing these information.
Although most previous skin-detection methods have used color cues almost exclusively, they are vulnerable to external factors (e.g., poor or unnatural illumination and skin tones).
In this work, we present a new approach based on U-Net that performs well in the absence of color information.
To be specific, we apply a new color space augmentation into the training stage to improve the performance of skin segmentation system over the illumination and skin tone diverse. The system was trained and tested with both original and color changed ECU dataset. We also test our system with RFW dataset, a larger dataset with four human races with different skin tones. The experimental results show improvements in evaluations for skin tones and complex illuminations.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/109445 |
| Date | 25 March 2022 |
| Creators | Xu, Han |
| Contributors | Electrical and Computer Engineering, Abbott, A. Lynn, Huang, Jia-Bin, Sarkar, Abhijit |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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