Generalizability and Application of the Skin Reflectance Estimate Based
  on Dichromatic Separation (SREDS)
Joseph Drahos Richard Plesh Keivan Bahmani Mahesh Banavar Stephanie Schuckers
Face recognition (FR) systems have become widely used and readily available
in recent history. However, differential performance between certain
demographics has been identified within popular FR models. Skin tone
differences between demographics can be one of the factors contributing to the
differential performance observed in face recognition models. Skin tone metrics
provide an alternative to self-reported race labels when such labels are
lacking or completely not available e.g. large-scale face recognition datasets.
In this work, we provide a further analysis of the generalizability of the Skin
Reflectance Estimate based on Dichromatic Separation (SREDS) against other skin
tone metrics and provide a use case for substituting race labels for SREDS
scores in a privacy-preserving learning solution. Our findings suggest that
SREDS consistently creates a skin tone metric with lower variability within
each subject and SREDS values can be utilized as an alternative to the
self-reported race labels at minimal drop in performance. Finally, we provide a
publicly available and open-source implementation of SREDS to help the research
community. Available at https://github.com/JosephDrahos/SREDS