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MODELING AND LEARNING CHARACTER TRAITS AND MEDICAL CONDITION BASED ON 3D FACIAL FEATURES

机译:基于3D面部特征的建模与学习特征及医学状况

摘要

A computer-implemented method for identifying character traits associated with a target subject includes acquiring image data of a target subject from an image data source, rendering a 3D image data set, comparing each of a plurality of regions of interest within the 3D image set to a historical image data set to identify active regions of interest, grouping subsets of the regions of interest into one or more convolutional feature layers, wherein each convolutional feature layer probabilistically maps to a pre- identified character trait, and applying a convolutional neural network model to the convolutional feature layers to identify a pattern of active regions of interest within each convolutional feature layer to predict whether a target subject possesses the pre- identified character trait.
机译:一种用于识别与目标对象关联的字符特征的计算机实现的方法,包括:从图像数据源获取目标对象的图像数据,渲染3D图像数据集,将3D图像集中的多个感兴趣区域中的每个区域进行比较,以得到历史图像数据集,用于识别感兴趣的活动区域,将感兴趣区域的子集分组到一个或多个卷积特征层中,其中每个卷积特征层概率性地映射到预先识别的字符特征,并将卷积神经网络模型应用于在卷积特征层中识别每个卷积特征层中感兴趣的活动区域的模式,以预测目标对象是否具有预先识别的字符特征。

著录项

  • 公开/公告号WO2018126275A1

    专利类型

  • 公开/公告日2018-07-05

    原文格式PDF

  • 申请/专利权人 DIRK SCHNEEMANN LLC;

    申请/专利号WO2018US12092

  • 发明设计人 SCHNEEMANN DIRK;

    申请日2018-01-02

  • 分类号G06T17/20;G06T15/04;G16H50/20;

  • 国家 WO

  • 入库时间 2022-08-21 12:43:27

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