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A subspace projection methodology for nonlinear manifold based face recognition.

机译：基于非线性流形的人脸识别的子空间投影方法。

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摘要

A novel feature extraction method that utilizes nonlinear mapping from the original data space to the feature space is presented in this dissertation. Feature extraction methods aim to find compact representations of data that are easy to classify. Measurements with similar values are grouped to same category, while those with differing values are deemed to be of separate categories. For most practical systems, the meaningful features of a pattern class lie in a low dimensional nonlinear constraint region (manifold) within the high dimensional data space. A learning algorithm to model this nonlinear region and to project patterns to this feature space is developed. Least squares estimation approach that utilizes interdependency between points in training patterns is used to form the nonlinear region. The proposed feature extraction strategy is employed to improve face recognition accuracy under varying illumination conditions and facial expressions. Though the face features show variations under these conditions, the features of one individual tend to cluster together and can be considered as a neighborhood. Low dimensional representations of face patterns in the feature space may lie in a nonlinear constraint region, which when modeled leads to efficient pattern classification. A feature space encompassing multiple pattern classes can be trained by modeling a separate constraint region for each pattern class and obtaining a mean constraint region by averaging all the individual regions. Unlike most other nonlinear techniques, the proposed method provides an easy intuitive way to place new points onto a nonlinear region in the feature space. The proposed feature extraction and classification method results in improved accuracy when compared to the classical linear representations.Face recognition accuracy is further improved by introducing the concepts of modularity, discriminant analysis and phase congruency into the proposed method. In the modular approach, feature components are extracted from different sub-modules of the images and concatenated to make a single vector to represent a face region. By doing this we are able to extract features that are more representative of the local features of the face. When projected onto an arbitrary line, samples from well formed clusters could produce a confused mixture of samples from all the classes leading to poor recognition. Discriminant analysis aims to find an optimal line orientation for which the data classes are well separated. Experiments performed on various databases to evaluate the performance of the proposed face recognition technique have shown improvement in recognition accuracy, especially under varying illumination conditions and facial expressions. This shows that the integration of multiple subspaces, each representing a part of a higher order nonlinear function, could represent a pattern with variability. Research work is progressing to investigate the effectiveness of subspace projection methodology for building manifolds with other nonlinear functions and to identify the optimum nonlinear function from an object classification perspective.
机译：本文提出了一种利用从原始数据空间到特征空间的非线性映射的特征提取方法。特征提取方法旨在找到易于分类的紧凑数据表示。具有相似值的测量被归为同一类别，而具有不同值的那些被视为不同的类别。对于大多数实际系统，模式类的有意义的特征在于高维数据空间内的低维非线性约束区域（歧管）。开发了一种学习算法，用于对该非线性区域进行建模并将模式投影到该特征空间。利用训练模式中点之间的相互依赖性的最小二乘估计方法用于形成非线性区域。提出的特征提取策略被用来提高在变化的照明条件和面部表情下的面部识别精度。尽管在这些条件下人脸特征显示出变化，但是一个人的特征倾向于聚集在一起，可以被视为邻居。特征空间中面部图案的低维表示可能位于非线性约束区域中，在建模时会导致有效的图案分类。可以通过为每个模式类别建模一个单独的约束区域并通过对所有单个区域取平均来获得平均约束区域来训练包含多个模式类别的特征空间。与大多数其他非线性技术不同，该方法提供了一种简单直观的方法来将新点放置在特征空间中的非线性区域上。与经典的线性表示相比，所提出的特征提取和分类方法提高了准确性。通过将模块化，判别分析和相位一致性的概念引入所提出的方法，可以进一步提高人脸识别的准确性。在模块化方法中，特征分量是从图像的不同子模块中提取出来的，并被连接起来以形成单个矢量来表示脸部区域。通过这样做，我们能够提取出更能代表面部局部特征的特征。当投影到任意线上时，来自形成良好的簇的样本可能会产生来自所有类别的样本的混淆混合物，从而导致识别不佳。判别分析的目的是找到一种最佳的行方向，将数据类别很好地分开。在各种数据库上进行的评估所提出的面部识别技术性能的实验表明，识别准确度有所提高，尤其是在光照条件和面部表情变化的情况下。这表明多个子空间的积分（每个代表一个高阶非线性函数的一部分）可以表示一个具有可变性的模式。研究工作正在进行中，以研究子空间投影方法在构建具有其他非线性函数的流形中的有效性，并从对象分类的角度确定最佳非线性函数。

著录项

  • 作者

    Sankaran, Praveen.;

  • 作者单位

    Old Dominion University.;

  • 授予单位 Old Dominion University.;
  • 学科 Engineering Computer.Computer Science.Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 87 p.
  • 总页数 87
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 古生物学;
  • 关键词

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