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Improving three-dimensional face recognition model generation and biometrics.

机译：改进三维人脸识别模型的生成和生物识别。

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摘要

3D face shape biometrics, with greater pose and lighting condition data invariance than 2D (photometric), has the potential to yield superior performance to 2D data for some applications. However, many of the capture limitations of 3D scanners are the same as those of 2D biometric capture devices with respect to lighting, environmental, and subject configurations. Many of the claimed advantages of 3D over 2D do not exist under current capture configurations. In addition, 3D scanners themselves are more expensive than 2D cameras, and 3D biometric data are unavailable on many of the subjects we would like to identify. Further, the significant computational cost of 3D face recognition has made large scale deployment of 3D face recognition impractical. The focus of this thesis is to address these issues to improve the feasability of 3D face recognition so that it is more applicable outside of a research environment. In particular, the focus is on improving the methods and hardware needed to produce a 3D model of a face, improving biometric recognition and verification performance, and decreasing the computational cost to allow for larger scale applications. In this thesis, I propose a new structure from motion approach, a new fast 3D face biometric, and examine the impact of movement on existing structure from light devices.
机译：3D人脸形状生物特征具有比2D（光度法）更大的姿势和光照条件数据不变性，在某些应用中具有产生优于2D数据的性能的潜力。但是，在照明，环境和对象配置方面，3D扫描仪的许多捕获限制与2D生物特征捕获设备的捕获限制相同。在当前的捕获配置下，3D优于2D的许多优势都不存在。此外，3D扫描仪本身比2D相机贵，并且在我们要识别的许多主题上都没有3D生物特征数据。此外，3D面部识别的巨大计算成本已经使得3D面部识别的大规模部署变得不切实际。本文的重点是解决这些问题，以提高3D人脸识别的可行性，使其在研究环境之外更适用。特别地，重点在于改进产生面部3D模型所需的方法和硬件，改善生物特征识别和验证性能以及降低计算成本以允许更大规模的应用。在本文中，我提出了一种基于运动方法的新结构，一种新的快速3D人脸生物识别技术，并研究了运动对照明设备中现有结构的影响。

著录项

  • 作者

    Boehnen, Christopher Bensing.;

  • 作者单位

    University of Notre Dame.;

  • 授予单位 University of Notre Dame.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 205 p.
  • 总页数 205
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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