图 书 馆
鲁棒性人耳人脸识别的三维变形模型构建3D Morphable Model Construction for Robust Ear and Face Recognition |
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| 课程网址: | http://videolectures.net/cvpr2010_bustard_3dmm/ |
| 主讲教师: | John David Bustard |
| 开课单位: | 南安普顿大学 |
| 开课时间: | 2010-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  最近的工作表明，人耳在不同的受试者之间差异很大，可用于识别。因此，原则上，在识别系统内除面部外使用耳朵还可以提高准确性和鲁棒性，特别是对于非正视图。本文介绍了使用基于头和耳朵的3D可变形模型的构建方法研究此假设的工作。创建包含耳朵的模型的一个问题是现有的训练数据集包含噪声和部分遮挡。除了手动排除这些区域外，还开发了使该过程自动化的分类器。当与强大的配准算法结合使用时，所得的系统可以使用较少约束的数据集有效地构建全头可变形模型。已经使用注册一致性，模型覆盖率和极简性度量对算法进行了评估，这些算法共同证明了该方法的准确性。为了更轻松地进行此工作，源代码已在线提供。 |
| 课程简介: | Recent work suggests that the human ear varies significantly between different subjects and can be used for identification. In principle, therefore, using ears in addition to the face within a recognition system could improve accuracy and robustness, particularly for non-frontal views. The paper describes work that investigates this hypothesis using an approach based on the construction of a 3D morphable model of the head and ear. One issue with creating a model that includes the ear is that existing training datasets contain noise and partial occlusion. Rather than exclude these regions manually, a classifier has been developed which automates this process. When combined with a robust registration algorithm the resulting system enables full head morphable models to be constructed efficiently using less constrained datasets. The algorithm has been evaluated using registration consistency, model coverage and minimalism metrics, which together demonstrate the accuracy of the approach. To make it easier to build on this work, the source code has been made available online. |
| 关 键 词: | 3D模型; 极简算法 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-03-25：zyk |
| 最后编审: | 2021-03-25：zyk |
| 阅读次数: | 43 |