Related papers: Analysis of Manual and Automated Skin Tone Assignments for Face Recognition Applications

Analysis of Manual and Automated Skin Tone Assignments for Face Recognition Applications

URL: http://arxiv.org/abs/2104.14685v1
Date: Thu, 29 Apr 2021 22:35:47 GMT
Title: Analysis of Manual and Automated Skin Tone Assignments for Face Recognition Applications
Authors: KS Krishnapriya, Michael C. King, Kevin W. Bowyer
Abstract summary: We analyze a set of manual Fitzpatrick skin type assignments and also employ the individual typology angle to automatically estimate the skin tone from face images. The level of agreement between automated and manual approaches is found to be 96% or better for the MORPH images.
Score: 8.334167427229572
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: News reports have suggested that darker skin tone causes an increase in face recognition errors. The Fitzpatrick scale is widely used in dermatology to classify sensitivity to sun exposure and skin tone. In this paper, we analyze a set of manual Fitzpatrick skin type assignments and also employ the individual typology angle to automatically estimate the skin tone from face images. The set of manual skin tone rating experiments shows that there are inconsistencies between human raters that are difficult to eliminate. Efforts to automate skin tone rating suggest that it is particularly challenging on images collected without a calibration object in the scene. However, after the color-correction, the level of agreement between automated and manual approaches is found to be 96% or better for the MORPH images. To our knowledge, this is the first work to: (a) examine the consistency of manual skin tone ratings across observers, (b) document that there is substantial variation in the rating of the same image by different observers even when exemplar images are given for guidance and all images are color-corrected, and (c) compare manual versus automated skin tone ratings.

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