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Automatic landmarking for non-cooperative 3D face recognition

机译:用于非协作式3D人脸识别的自动标记

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摘要

This thesis describes a new framework for 3D surface landmarking and evaluates its performance for feature localisation on human faces. This framework has two main parts that can be designed and optimised independently. The first one is a keypoint detection system that returns positions of interest for a given mesh surface by using a learnt dictionary of local shapes. The second one is a labelling system, using model fitting approaches that establish a one-to-one correspondence between the set of unlabelled input points and a learnt representation of the class of object to detect. Our keypoint detection system returns local maxima over score maps that are generated from an arbitrarily large set of local shape descriptors. The distributions of these descriptors (scalars or histograms) are learnt for known landmark positions on a training dataset in order to generate a model. The similarity between the input descriptor value for a given vertex and a model shape is used as a descriptor-related score. Our labelling system can make use of both hypergraph matching techniques and rigid registration techniques to reduce the ambiguity attached to unlabelled input keypoints for which a list of model landmark candidates have been seeded. The soft matching techniques use multi-attributed hyperedges to reduce ambiguity, while the registration techniques use scale-adapted rigid transformation computed from 3 or more points in order to obtain one-to-one correspondences. Our final system achieves better or comparable (depending on the metric) results than the state-of-the-art while being more generic. It does not require pre-processing such as cropping, spike removal and hole filling and is more robust to occlusion of salient local regions, such as those near the nose tip and inner eye corners. It is also fully pose invariant and can be used with kinds of objects other than faces, provided that labelled training data is available.
机译:本文介绍了一种用于3D表面标记的新框架,并评估了其在人脸特征定位方面的性能。该框架有两个主要部分,可以独立设计和优化。第一个是关键点检测系统,该系统通过使用学习到的局部形状字典返回给定网格表面的感兴趣位置。第二个是标记系统,它使用模型拟合方法在一组未标记的输入点和要检测的对象类别的学习表示之间建立一对一的对应关系。我们的关键点检测系统返回分数图的局部最大值,该分数图是从任意大的一组局部形状描述符生成的。对于训练数据集上的已知界标位置,要学习这些描述符的分布(标量或直方图),以便生成模型。给定顶点的输入描述符值和模型形状之间的相似度用作与描述符相关的分数。我们的标记系统可以同时使用超图匹配技术和刚性注册技术,以减少与未标记输入关键点相关联的歧义,为此输入了模型地标候选列表。软匹配技术使用多属性超边缘来减少歧义,而配准技术则使用从3个或更多点计算出的比例自适应刚性变换,以获得一对一的对应关系。我们的最终系统在实现通用性的同时,与最新技术相比,可以获得更好或更可比的结果(取决于指标)。它不需要诸如裁剪,去除穗状花序和填充孔的预处理,并且对于遮盖显着的局部区域(例如靠近鼻尖和内眼角的区域)更健壮。它也完全是姿势不变的,并且可以用于除面部以外的其他各种对象,前提是可以使用标记的训练数据。

著录项

  • 作者

    Creusot Clement;

  • 作者单位
  • 年度 2011
  • 总页数
  • 原文格式 PDF
  • 正文语种 English
  • 中图分类

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