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Selection and Extraction of Patch Descriptors for 3D Face Recognition

机译:用于3D人脸识别的补丁描述符的选择和提取

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摘要

In 3D face recognition systems, 3D facial shape information plays an important role. 3D face recognizers usually depend on point cloud representation of faces where faces are represented as a set of 3D point coordinates. In many of the previous studies, faces are represented holistically and the discriminative contribution of local regions are assumed to be equivalent. In this work, we aim to design a local region-based 3D face representation scheme where the discriminative contribution of local facial regions are taken into account by using a subset selection mechanism. In addition to the subset selection methodology, we have extracted patch descriptors and coded them using Linear Discriminant Analysis (LDA). Our experiments on the 3D_RMA database show that both the proposed floating backward subset selection scheme and the LDA-based coding of region descriptors improve the classification accuracy, and reduce the representation complexity significantly.
机译:在3D人脸识别系统中,3D人脸形状信息起着重要作用。 3D人脸识别器通常取决于人脸的点云表示,其中人脸表示为一组3D点坐标。在许多以前的研究中,人脸是整体代表的,并且假定局部区域的歧视性贡献是相等的。在这项工作中,我们旨在设计一种基于局部区域的3D人脸表示方案,其中通过使用子集选择机制考虑了局部面部区域的歧视性贡献。除了子集选择方法外,我们还提取了补丁描述符,并使用线性判别分析(LDA)对其进行了编码。我们在3D_RMA数据库上的实验表明,所提出的浮动后向子集选择方案和基于LDA的区域描述符编码均可提高分类精度,并显着降低表示复杂度。

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