Skin colour typology and suntanning pathways
By A. Chardon, I. Cretois, C. Hourseau. 1991
Subfields: Medical & Health AI
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Medical & Health AI
- Klint Qinami
- Mohit Bansal
- Jia Deng
- Rogério Schmidt Feris
- Abhaysinh Zala
- John R. Smith
- Olga Russakovsky
- Michele Merler
Citations in the corpus
- Towards fairer datasets: filtering and balancing the distribution of the people subtree in the ImageNet hierarchy (2019, methodology): “We annotate skin color according to an established dermatological metric—individual typology angle (ITA) [16].”
- Diversity in Faces (2019, methodology): “As in [7], we implemented ITA in the CIE-Lab space.”
- Measurement of skin color: practical application and theoretical considerations. (1998, background): “that (a) L*(30), b*(31), or a combination of L* and b*(32) are reasonable parameters for evaluation of the degree of pigmentation, if the cutaneous blood volume is considered to be similar at all test sites (b) The melanin index is a good parameter for pigmentation, which is less influenced by cutan”
- Fitzpatrick Skin Type, Individual Typology Angle, and Melanin Index in an African Population: Steps Toward Universally Applicable Skin Photosensitivity Assessments. (2015, background): “Because the current procedure for assigning FST relies on perception of how skin burns and/or tans, participants identified whether their skin (1) burned without tanning, (2) burned and then tanned, or (3) only tanned after initial sun exposure.”
- DALL-EVAL: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models (2022, methodology): “Then we calculate Individual Typology Angle (ITA) [13] and find the closest skin tone in the MST scale [40].”