公开(公告)号：US20220075988A1

公开(公告)日：2022-03-10

申请号：US17528294

申请日：2021-11-17

Applicant: L'Oreal

Inventor： Tian Xing LI ,  Zhi YU ,  Irina KEZELE ,  Edmund PHUNG ,  Parham AARABI

IPC: G06K9/00 ,  G06T7/143 ,  G06T7/11 ,  G06N3/04 ,  G06N3/08 ,  G06T5/20 ,  G06T19/00

Abstract: There are provided systems and methods for facial landmark detection using a convolutional neural network (CNN). The CNN comprises a first stage and a second stage where the first stage produces initial heat maps for the landmarks and initial respective locations for the landmarks. The second stage processes the heat maps and performs Region of Interest-based pooling while preserving feature alignment to produce cropped features. Finally, the second stage predicts from the cropped features a respective refinement location offset to each respective initial location. Combining each respective initial location with its respective refinement location offset provides a respective final coordinate (x,y) for each respective landmark in the image. Two-stage localization design helps to achieve fine-level alignment while remaining computationally efficient. The resulting architecture is both small enough in size and inference time to be suitable for real-time web applications such as product simulation and virtual reality.

公开(公告)号：US20230123037A1

公开(公告)日：2023-04-20

申请号：US18080331

申请日：2022-12-13

Applicant: L'OREAL

Inventor： Ruowei JIANG ,  Junwei MA ,  He MA ,  Eric ELMOZNINO ,  Irina KEZELE ,  Alex LEVINSHTEIN ,  Julien DESPOIS ,  Matthieu PERROT ,  Frederic Antoinin Raymond Serge FLAMENT ,  Parham AARABI

IPC: A61B5/00 ,  G06T7/00 ,  G06V40/16 ,  G06N3/08 ,  G06N3/045

Abstract: 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.

公开(公告)号：US20200160153A1

公开(公告)日：2020-05-21

申请号：US16683398

申请日：2019-11-14

Applicant: L'Oreal

Inventor： Eric ELMOZNINO ,  He MA ,  Irina KEZELE ,  Edmund PHUNG ,  Alex LEVINSHTEIN ,  Parham AARABI

IPC: G06N3/04 ,  G06T19/00 ,  G06N20/00 ,  G06N3/08 ,  G06F3/01

Abstract: Systems and methods relate to a network model to apply an effect to an image such as an augmented reality effect (e.g. makeup, hair, nail, etc.). The network model uses a conditional cycle-consistent generative image-to-image translation model to translate images from a first domain space where the effect is not applied and to a second continuous domain space where the effect is applied. In order to render arbitrary effects (e.g. lipsticks) not seen at training time, the effect's space is represented as a continuous domain (e.g. a conditional variable vector) learned by encoding simple swatch images of the effect, such as are available as product swatches, as well as a null effect. The model is trained end-to-end in an unsupervised fashion. To condition a generator of the model, convolutional conditional batch normalization (CCBN) is used to apply the vector encoding the reference swatch images that represent the makeup properties.

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.

公开(公告)号：US20200320748A1

公开(公告)日：2020-10-08

申请号：US16753214

申请日：2018-10-24

Applicant: L'OREAL

Inventor： Alex LEVINSHTEIN ,  Cheng CHANG ,  Edmund PHUNG ,  Irina KEZELE ,  Wenzhangzhi GUO ,  Eric ELMOZNINO ,  Ruowei JIANG ,  Parham AARABI

IPC: G06T11/00 ,  G06T7/90 ,  G06T1/20 ,  G06T7/11 ,  G06K9/62 ,  G06K9/46 ,  G06K9/00 ,  G06N3/08

Abstract: A system and method implement deep learning on a mobile device to provide a convolutional neural network (CNN) for real time processing of video, for example, to color hair. Images are processed using the CNN to define a respective hair matte of hair pixels. The respective object mattes may be used to determine which pixels to adjust when adjusting pixel values such as to change color, lighting, texture, etc. The CNN may comprise a (pre-trained) network for image classification adapted to produce the segmentation mask. The CNN may be trained for image segmentation (e.g. using coarse segmentation data) to minimize a mask-image gradient consistency loss. The CNN may further use skip connections between corresponding layers of an encoder stage and a decoder stage where shallower layers in the encoder, which contain high-res but weak features are combined with low resolution but powerful features from deeper decoder layers.

公开(公告)号：US20230169571A1

公开(公告)日：2023-06-01

申请号：US18102139

申请日：2023-01-27

Applicant: L'OREAL

Inventor： Eric ELMOZNINO ,  Irina KEZELE ,  Parham AARABI

IPC: G06Q30/0601 ,  G06T5/50 ,  G06N20/00 ,  G06F18/214 ,  G06V10/764 ,  G06V10/774 ,  G06V10/778

CPC classification number: G06Q30/0631 ,  G06T5/50 ,  G06N20/00 ,  G06F18/214 ,  G06V10/764 ,  G06V10/774 ,  G06V10/7788

Abstract: Techniques are provided for computing systems, methods and computer program products to produce efficient image-to-image translation by adapting unpaired datasets for supervised learning. A first model (a powerful model) may be defined and conditioned using unsupervised learning to produce a synthetic paired dataset from the unpaired dataset, translating images from a first domain to a second domain and images from the second domain to the first domain. The synthetic data generated is useful as ground truths in supervised learning. The first model may be conditioned to overfit the unpaired dataset to enhance the quality of the paired dataset (e.g. the synthetic data generated). A run-time model such as for a target device is trained using the synthetic paired dataset and supervised learning. The run-time model is small and fast to meet the processing resources of the target device (e.g. a personal user device such as a smart phone, tablet, etc.)