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1. 发明授权

US09129147B1 Optimal automatic capture of facial movements and expressions in video sequences 有权
标题翻译：最佳自动捕获视频序列中的面部动作和表情
公开(公告)号：US09129147B1
公开(公告)日：2015-09-08
申请号：US13795154
申请日：2013-03-12
申请人： Image Metrics Ltd.
发明人： Michael Rogers , Kevin Walker , Tomos G. Williams , Martin De La Gorce , Martin Tosas
IPC分类号： G06K9/00
CPC分类号： G06K9/00308
摘要： Computerized methods for creating tracks of locations across frames of a video corresponding to a facial feature of a human. A set of feature location hypotheses is generated, as applied to images derived from the sequence of frames representing images of the human. Each hypothesis is refined, and a first set of confidence measures is associated with each hypothesis. A second set of confidence measures is associated with interframe transition, and a cost function that is a combination of hypotheses and transition confidence measures is minimized. A set of tracks is generated, characterizing each of a plurality of facial features within each frame of the sequence of frames. Performance analysis data may further be derived in a performance driven animation production pipeline, based on the generated tracks.
摘要翻译：用于创建与人的面部特征相对应的视频的帧的位置的轨迹的计算机化方法。生成一组特征位置假设，应用于从表示人的图像的帧的序列导出的图像。每个假设被改进，并且第一组置信度量度与每个假设相关联。第二组置信度量度与帧间转换相关联，并且作为假设和转换置信度度量的组合的成本函数被最小化。生成一组轨迹，表征帧序列的每个帧内的多个面部特征中的每一个。基于生成的轨迹，可以在性能驱动的动画制作流水线中进一步导出性能分析数据。

2. 发明授权

US09111134B1 Building systems for tracking facial features across individuals and groups 有权
标题翻译：用于跟踪个人和团体的面部特征的建筑系统
公开(公告)号：US09111134B1
公开(公告)日：2015-08-18
申请号：US13795882
申请日：2013-03-12
申请人： Image Metrics Ltd.
发明人： Michael Rogers , Kevin Walker , Tomos G. Williams , Martin DeLa Gorce , Martin Tosas
IPC分类号： G06K9/46 , G06K9/00
CPC分类号： G06K9/00302 , G06K9/00315 , G06K9/621
摘要： Computer implemented methods for generating a non-transient record of feature locations and/or facial expression parameters characterizing a person's face. A video sequence of a specified individual person is received and a feature locator update model is applied to the video sequence. The feature locator update model is derived by defining a set of training images, generating a set of facial feature displacements for each training image with associated image sample vectors, and training a regularized linear regression which maps from image sample vectors to displacement vectors, wherein the regularization includes a spatial smoothness term within the shape-free sample space. A feature location and/or a facial expression parameter is then extracted, based on the feature update model, characterizing the location, and/or the expression, of the feature of the face of the specified individual person.
摘要翻译：计算机实现的方法，用于产生特征位置的非瞬时记录和/或表征人脸的面部表情参数。接收指定个人的视频序列，并将特征定位器更新模型应用于视频序列。通过定义一组训练图像来产生特征定位器更新模型，为相关联的图像样本向量为每个训练图像生成一组面部特征位移，以及训练从图像样本向量映射到位移矢量的正则化线性回归，其中正则化包括无形样本空间内的空间平滑度项。然后基于特征更新模型提取特征位置和/或面部表情参数，表征特定个人的面部特征的位置和/或表达。

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