L'oreal

Elmoznino, Eric

Flament, Frederic Antoinin Raymond Serge

Jiang, Ruowei

Kezele, Irina

Ma, Junwei

Levinshtein, Alex

Despois, Julien

Aarabi, Parham

Ma, He

Perrot, Matthieu

Matthieu Perrot

Robin Kips

Julien Despois

Emmanuel Malherbe

Parham Aarabi

Irina Kezele

Alex Levinshtein

Helga Malaprade

Ruowei Jiang

Grégoire Charraud

Géraldine Thiebaut

Eric Elmoznino

Loic Tran

Frederic Antoinin Raymond Serge Flament

Junwei Ma

He Ma

Christine Elfakhri

Florent Valceschini

Frédéric Flament

Panagiotis-alexandros Bokaris

Thierry Wasserman

John Charbit

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

The present application is directed to a method and system for determining at least one physical and/or chemical characteristic of a keratinous surface of a user, the method comprising the steps of: —receiving data corresponding to at least one image of the keratinous surface, —processing the image by applying at least one machine learning model to said image, —returning at least one numerical value corresponding to a grade of the characteristic of the keratinous surface to be determined.

Method and system for determining a characteristic of a keratinous surface and method and system for treating said keratinous surface

There are provided computing devices and methods, etc. to controllably transform an image of a face, including a high resolution image, to simulate continuous aging. Ethnicity-specific aging information and weak spatial supervision are used to guide the aging process defined through training a model comprising a GANs based generator. Aging maps present the ethnicity-specific aging information as skin sign scores or apparent age values. The scores are located in the map in association with a respective location of the skin sign zone of the face associated with the skin sign. Patch-based training, particularly in association with location information to differentiate similar patches from different parts of the face, is used to train on high resolution images while minimize resource usage.

High-resolution controllable face aging with spatially-aware conditional GANs

There is shown and described a deep learning based system and method for skin diagnostics as well as testing metrics that show that such a deep learning based system outperforms human experts on the task of apparent skin diagnostics. Also shown and described is a system and method of monitoring a skin treatment regime using a deep learning based system and method for skin diagnostics.

Automatic image-based skin diagnostics using deep learning

Embodiments of the present disclosure provide systems, methods, and computer-readable media that use machine learning models to enable computing devices to detect and identify cosmetic products in face images. In some embodiments, a model training system may gather training data for building the machine learning models by analyzing face images associated with tagging data. In some embodiments, a recommendation system may be configured to use the machine learning models generated by the model training system to detect products in face images, and to add information based on the detected products to a look data store, and/or to provide recommendations for similar looks from the look data store based on the detected products.

Systems and methods for providing personalized product recommendations using deep learning

Growing community of inventors

Orsay, France

Matthieu Perrot