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Subspace representations for robust face and facial expression recognition.

机译：用于健壮的面部和面部表情识别的子空间表示。

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摘要

Analyzing human faces and modeling their variations have always been of interest to the computer vision community. Face analysis based on 2D intensity images is a challenging problem, complicated by variations in pose, lighting, blur, and non-rigid facial deformations due to facial expressions. Among the different sources of variation, facial expressions are of interest as important channels of non-verbal communication. Facial expression analysis is also affected by changes in view-point and inter-subject variations in performing different expressions. This dissertation makes an attempt to address some of the challenges involved in developing robust algorithms for face and facial expression recognition by exploiting the idea of proper subspace representations for data.;Variations in the visual appearance of an object mostly arise due to changes in illumination and pose. So we first present a video-based sequential algorithm for estimating the face albedo as an illumination-insensitive signature for face recognition. We show that by knowing/estimating the pose of the face at each frame of a sequence, the albedo can be efficiently estimated using a Kalman filter. Then we extend this to the case of unknown pose by simultaneously tracking the pose as well as updating the albedo through an efficient Bayesian inference method performed using a Rao-Blackwellized particle filter.;Since understanding the effects of blur, especially motion blur, is an important problem in unconstrained visual analysis, we then propose a blur-robust recognition algorithm for faces with spatially varying blur. We model a blurred face as a weighted average of geometrically transformed instances of its clean face. We then build a matrix, for each gallery face, whose column space spans the space of all the motion blurred images obtained from the clean face. This matrix representation is then used to define a proper objective function and perform blur-robust face recognition.;To develop robust and generalizable models for expression analysis one needs to break the dependence of the models on the choice of the coordinate frame of the camera. To this end, we build models for expressions on the affine shape-space (Grassmann manifold), as an approximation to the projective shape-space, by using a Riemannian interpretation of deformations that facial expressions cause on different parts of the face. This representation enables us to perform various expression analysis and recognition algorithms without the need for pose normalization as a preprocessing step.;There is a large degree of inter-subject variations in performing various expressions. This poses an important challenge on developing robust facial expression recognition algorithms. To address this challenge, we propose a dictionary-based approach for facial expression analysis by decomposing expressions in terms of action units (AUs). First, we construct an AU-dictionary using domain experts' knowledge of AUs. To incorporate the high-level knowledge regarding expression decomposition and AUs, we then perform structure-preserving sparse coding by imposing two layers of grouping over AU-dictionary atoms as well as over the test image matrix columns. We use the computed sparse code matrix for each expressive face to perform expression decomposition and recognition.;Most of the existing methods for the recognition of faces and expressions consider either the expression-invariant face recognition problem or the identity-independent facial expression recognition problem. We propose joint face and facial expression recognition using a dictionary-based component separation algorithm (DCS). In this approach, the given expressive face is viewed as a superposition of a neutral face component with a facial expression component, which is sparse with respect to the whole image. This assumption leads to a dictionary-based component separation algorithm, which benefits from the idea of sparsity and morphological diversity. The DCS algorithm uses the data-driven dictionaries to decompose an expressive test face into its constituent components. The sparse codes we obtain as a result of this decomposition are then used for joint face and expression recognition.
机译：分析人脸并对其变化进行建模一直是计算机视觉界的兴趣所在。基于2D强度图像的面部分析是一个具有挑战性的问题，其复杂性包括姿势，光照，模糊和由于面部表情引起的非刚性面部变形。在变异的不同来源中，面部表情作为非语言交流的重要渠道而受到关注。面部表情分析还受执行不同表情时视点变化和对象间差异的影响。本论文试图通过利用数据的适当子空间表示的思想来解决开发用于面部和面部表情识别的鲁棒算法所涉及的一些挑战。;对象的视觉外观变化主要是由于照明和光照的变化引起的。姿势因此，我们首先提出一种基于视频的顺序算法，用于估计面部反照率，作为对面部识别不敏感的照明签名。我们表明，通过知道/估计序列每一帧的人脸姿势，可以使用卡尔曼滤波器有效地估计反照率。然后，我们通过同时跟踪姿势以及通过使用Rao-Blackwellized粒子滤波器执行的有效贝叶斯推理方法更新反照率，将其扩展到未知姿势的情况；因为了解模糊（尤其是运动模糊）的影响是无约束视觉分析中的重要问题，我们针对空间变​​化的模糊提出了一种模糊鲁棒识别算法。我们将模糊的面孔建模为其干净面孔的几何变换实例的加权平均值。然后，我们为每个画廊的面孔构建一个矩阵，其列空间跨越从干净的面孔获得的所有运动模糊图像的空间。然后，该矩阵表示用于定义适当的目标函数并执行模糊-鲁棒的人脸识别。为了开发用于表达式分析的鲁棒且通用的模型，需要打破模型对相机坐标系选择的依赖。为此，我们通过仿射对面部表情在面部不同部位引起的变形的黎曼式解释，建立仿射形状空间（格拉斯曼流形）上表情的模型，作为射影形状空间的近似值。这种表示使我们能够执行各种表情分析和识别算法，而无需将姿势归一化作为预处理步骤。;在执行各种表情时主体间存在很大程度的差异。这对开发鲁棒的面部表情识别算法提出了重大挑战。为了解决这一挑战，我们提出了一种基于字典的面部表情分析方法，通过按照动作单位（AU）分解表情。首先，我们使用领域专家对AU的知识来构建AU词典。为了合并有关表达式分解和AU的高级知识，我们然后通过在AU字典原子以及测试图像矩阵列上施加两层分组来执行保留结构的稀疏编码。我们对每个有表情的脸使用计算的稀疏代码矩阵来进行表情分解和识别。大多数现有的表情和表情识别方法都考虑了表情不变的脸识别问题或身份无关的表情识别问题。我们提出使用基于字典的成分分离算法（DCS）进行面部和面部表情的联合识别。在这种方法中，给定的表情面部被视为中性面部成分与面部表情成分的叠加，相对于整个图像而言，面部表情成分比较稀疏。这种假设导致了基于字典的成分分离算法，该算法得益于稀疏性和形态多样性的思想。 DCS算法使用数据驱动的字典将可表达的测试面分解为其组成部分。我们将由于分解而获得的稀疏代码用于面部和表情的联合识别。

著录项

  • 作者

    Taheri, Sima.;

  • 作者单位

    University of Maryland, College Park.;

  • 授予单位 University of Maryland, College Park.;
  • 学科 Computer Science.;Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 209 p.
  • 总页数 209
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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