公开(公告)号：US20210406996A1

公开(公告)日：2021-12-30

申请号：US17361743

申请日：2021-06-29

Applicant: L'Oreal

Inventor： Zhi YU ,  Yuze ZHANG ,  Ruowei JIANG ,  Jeffrey HOUGHTON ,  Parham AARABI ,  Frederic FLAMENT

IPC: G06Q30/06 ,  G06N3/04 ,  G06K9/00 ,  G06K9/62

Abstract: There is described a deep learning supervised regression based model including methods and systems for facial attribute prediction and use thereof. An example of use is an augmented and/or virtual reality interface to provide a modified image responsive to facial attribute predictions determined from the image. Facial effects matching facial attributes are selected to be applied in the interface

公开(公告)号：US20210407153A1

公开(公告)日：2021-12-30

申请号：US17363098

申请日：2021-06-30

Applicant: L'Oreal

Inventor： Julien DESPOIS ,  Frederic FLAMENT ,  Matthieu PERROT

IPC: G06T11/00 ,  G06K9/00 ,  G06T7/00 ,  G06Q30/06 ,  G06N3/04 ,  G06N3/08 ,  A45D44/00

Abstract: 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.

公开(公告)号：US20210012493A1

公开(公告)日：2021-01-14

申请号：US16996087

申请日：2020-08-18

Applicant: L'Oreal

Inventor： Ruowei JIANG ,  Irina KEZELE ,  Zhi Yu ,  Sophie SEITE ,  Frederic FLAMENT ,  Parham AARABI

IPC: G06T7/00 ,  G16H50/20 ,  G06Q30/06 ,  A61B5/00

Abstract: Systems and methods process images to determine a skin condition severity analysis and to visualize a skin analysis such as using a deep neural network (e.g. a convolutional neural network) where a problem was formulated as a regression task with integer-only labels. Auxiliary classification tasks (for example, comprising gender and ethnicity predictions) are introduced to improve performance. Scoring and other image processing techniques may be used (e.g. in assoc. with the model) to visualize results such as highlighting the analyzed image. It is demonstrated that the visualization of results, which highlight skin condition affected areas, can also provide perspicuous explanations for the model. A plurality (k) of data augmentations may be made to a source image to yield k augmented images for processing. Activation masks (e.g. heatmaps) produced from processing the k augmented images are used to define a final map to visualize the skin analysis.