* External authors




Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color

William Thong

Przemyslaw Joniak*

Alice Xiang

* External authors

ICCV 2023



This paper strives to measure apparent skin color in computer vision, beyond a unidimensional scale on skin tone. In their seminal paper Gender Shades, Buolamwini and Gebru have shown how gender classification systems can be biased against women with darker skin tones. While the Fitzpatick skin type classification is commonly used to measure skin color, it only focuses on the skin tone ranging from light to dark. Subsequently, fairness researchers and practitioners have adopted the Fitzpatick skin type classification as a common measure to assess skin color bias in computer vision systems. While effective, the Fitzpatick scale only focuses on the skin tone ranging from light to dark. Towards a more comprehensive measure of skin color, we introduce the hue angle ranging from red to yellow. When applied to images, the hue dimension reveals additional biases related to skin color in both computer vision datasets and models. We then recommend multidimensional skin color scales, relying on both skin tone and hue, for fairness assessments.

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