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Two- and Three-Dimensional Face Recognition under Expression Variation.

机译：表情变化下的二维和三维人脸识别。

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摘要

In this thesis, the expression variation problem in two-dimensional (2D) and three-dimensional (3D) face recognition is tackled. While discriminant analysis (DA) methods are effective solutions for recognizing expression-variant 2D face images, they are not directly applicable when only a single sample image per subject is available. This problem is addressed in this thesis by introducing expression subspaces which can be used for synthesizing new expression images from subjects with only one sample image. It is proposed that by augmenting a generic training set with the gallery and their synthesized new expression images, and then training DA methods using this new set, the face recognition performance can be significantly improved. An important advantage of the proposed method is its simplicity; the expression of an image is transformed simply by projecting it into another subspace. The above proposed solution can also be used in general pattern recognition applications.;The above method can also be used in 3D face recognition where expression variation is a more serious issue. However, DA methods cannot be readily applied to 3D faces because of the lack of a proper alignment method for 3D faces. To solve this issue, a method is proposed for sampling the points of the face that correspond to the same facial features across all faces, denoted as the closest-normal points (CNPs). It is shown that the performance of the linear discriminant analysis (LDA) method, applied to such an aligned representation of 3D faces, is significantly better than the performance of the state-of-the-art methods which, rely on one-by-one registration of the probe faces to every gallery face. Furthermore, as an important finding, it is shown that the surface normal vectors of the face provide a higher level of discriminatory information rather than the coordinates of the points.;In addition, the expression subspace approach is used for the recognition of 3D faces from single sample. By constructing expression subspaces from the surface normal vectors at the CNPs, the surface normal vectors of a 3D face with single sample can be synthesized under other expressions. As a result, by improving the estimation of the within-class scatter matrix using the synthesized samples, a significant improvement in the recognition performance is achieved.
机译：本文解决了二维（2D）和三维（3D）人脸识别中的表情变化问题。尽管判别分析（DA）方法是识别表情变化的2D面部图像的有效解决方案，但当每个对象只有一个样本图像可用时，它们并不直接适用。本文通过引入表达子空间解决了这一问题，该子空间可用于从仅具有一个样本图像的对象合成新的表达图像。建议通过用图库及其合成的新表达图像来增强通用训练集，然后使用该新训练集训练DA方法，可以显着提高人脸识别性能。所提出的方法的重要优点是它的简单性。只需将图像投影到另一个子空间中，即可变换图像的表达方式。以上提出的解决方案也可以用于一般模式识别应用中。上述方法也可以用于表情变化更为严重的3D人脸识别中。但是，由于缺少针对3D面的正确对齐方法，因此DA方法无法轻松应用于3D面。为了解决该问题，提出了一种方法，该方法用于在所有脸部上采样与相同脸部特征相对应的脸部点，称为最接近法线点（CNP）。结果表明，线性判别分析（LDA）方法应用于这种3D人脸的对齐表示的性能明显优于依靠一种方法的最新方法的性能。探针面在每个图库面上都有一个配准。此外，作为一个重要发现，表明人脸的表面法线向量提供了更高级别的区分信息，而不是点的坐标。;此外，表达式子空间方法用于从3D人脸识别中单个样本。通过从CNP处的表面法线向量构建表达子空间，可以在其他表达式下合成具有单个样本的3D面的表面法线向量。结果，通过改进使用合成样本的类内散射矩阵的估计，实现了识别性能的显着改善。

著录项

  • 作者

    Mohammadzade, Narges Hoda.;

  • 作者单位

    University of Toronto (Canada).;

  • 授予单位 University of Toronto (Canada).;
  • 学科 Engineering Electronics and Electrical.;Computer Science.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 146 p.
  • 总页数 146
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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