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Limitations of ITA for Skin Type Estimation Under Uncontrolled Imaging Conditions
Neda Alipour1, Ted Burke1, Jane Courtney1
1School of Electrical and Electronic Engineering, Technological University Dublin, Dublin, Ireland.
Summary
Individual Typology Angle (ITA) is unreliable for classifying Fitzpatrick skin types (FST) in diverse datasets. This image-derived metric shows poor accuracy and high sensitivity to lighting, failing to capture true skin color diversity.
Area of Science:
- Computer Vision
- Dermatology
- Medical Imaging
Background:
- Accurate skin color assessment is crucial for diverse AI training datasets.
- Traditional Fitzpatrick scale may not capture skin color complexity.
- Quantitative methods for skin color variation need validation under real-world conditions.
Purpose of the Study:
- Evaluate the reliability of Individual Typology Angle (ITA) for Fitzpatrick skin type (FST) classification.
- Assess ITA's performance using a dermatologist-labeled dataset (PAD-UFES-20).
- Determine if ITA is a suitable proxy for skin type in uncontrolled imaging.
Main Methods:
- Utilized the PAD-UFES-20 dataset with dermatologist-assigned FST labels.
- Extracted skin color features and calculated ITA from skin patch images.
- Analyzed ITA distribution across FST categories and evaluated classification accuracy.
Main Results:
- Significant overlap in ITA distributions across FST categories was observed.
- Fixed-threshold ITA classification accuracy was 22.9%, below the majority baseline (51.3%).
- ITA failed to classify the darkest skin type (FST VI) and was unstable due to lighting variations.
Conclusions:
- Fixed-threshold ITA is not a reliable method for FST classification under uncontrolled lighting.
- ITA is highly sensitive to lighting and does not accurately reflect dermatologist-assigned FST labels.
- ITA has limitations as a proxy for skin type assessment in image datasets.
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