公开(公告)号：US20160148247A1

公开(公告)日：2016-05-26

申请号：US14921725

申请日：2015-10-23

Applicant: Intel Corporation

Inventor： JIANGUO LI ,  TAO WANG ,  YANGZHOU DU ,  QIANG LI ,  YIMIN ZHANG

IPC: G06Q30/02 ,  G06K9/00

CPC classification number: G06Q30/0242 ,  G06K9/00228 ,  G06K9/00281 ,  G06K9/00288 ,  G06K9/00302 ,  G06K2009/00322 ,  G06Q30/0251 ,  G06Q30/0255 ,  G06Q30/0267 ,  G06Q30/0269

Abstract: A system and method for selecting an advertisement to present to a consumer includes detecting facial regions in the image, identifying one or more consumer characteristics (mood, gender, age, etc.) of said consumer in the image, identifying one or more advertisements to present to the consumer based on a comparison of the consumer characteristics with an advertisement database including a plurality of advertisement profiles, and presenting a selected one of the identified advertisement to the consumer on a media device.

Abstract translation: 用于选择呈现给消费者的广告的系统和方法包括检测图像中的面部区域，识别图像中的所述消费者的一个或多个消费者特征（情绪，性别，年龄等），识别一个或多个广告 基于消费者特征与包括多个广告简档的广告数据库的比较，以及在所述媒体设备上向所述消费者呈现所识别的广告中选择的一个。

公开(公告)号：US20170256086A1

公开(公告)日：2017-09-07

申请号：US15124811

申请日：2015-12-18

Applicant: INTEL CORPORATION

Inventor： MINJE PARK ,  TAE-HOON KIM ,  MYUNG-HO JU ,  JIHYEON YI ,  XIAOLU SHEN ,  LIDAN ZHANG ,  QIANG LI

IPC: G06T13/40 ,  G06T7/73

CPC classification number: G06T13/40 ,  G06T7/73 ,  G06T17/20 ,  G06T2207/20084 ,  G06T2207/30201 ,  G06T2210/44

Abstract: Avatar animation systems disclosed herein provide high quality, real-time avatar animation that is based on the varying countenance of a human face. In some example embodiments, the real-time provision of high quality avatar animation is enabled at least in part, by a multi-frame regressor that is configured to map information descriptive of facial expressions depicted in two or more images to information descriptive of a single avatar blend shape. The two or more images may be temporally sequential images. This multi-frame regressor implements a machine learning component that generates the high quality avatar animation from information descriptive of a subject's face and/or information descriptive of avatar animation frames previously generated by the multi-frame regressor. The machine learning component may be trained using a set of training images that depict human facial expressions and avatar animation authored by professional animators to reflect facial expressions depicted in the set of training images.