L'oreal

Christine Elfakhri

Florent Valceschini

Matthieu Perrot

Loic Tran

Emmanuel Malherbe

Robin Kips

Guive Balooch

Zhiyuan Song

Hemant Joshi

Yuanjie Li

Dominique Moyal

In some embodiments of the present disclosure, one or more machine learning models are trained to accurately estimate skin color in one or more images regardless of the lighting conditions. In some embodiments, the models can then be used to estimate a skin color in a new image, and that estimated skin color can be used for a variety of purposes. For example, the skin color may be used to generate a recommendation for a foundation shade that accurately matches the skin color, or a recommendation for another cosmetic product that is complimentary with the estimated skin color. Thus, the need for an in-person test of the product is eliminated.

Techniques for identifying skin color in images having uncontrolled lighting conditions

An image is accepted by one or more processing circuits from a user depicting the user's skin. Machine learning models stored in one or more memory circuits are applied to the image to classify acne characteristics. An acne severity grade is provided by the one or more processing circuits and a user interface displays the acne severity grade.

Machine-implemented acne grading

Growing community of inventors

Brooklyn, NY, United States of America

Christine Elfakhri