Face Recognition in Subspaces
Face Recognition in Subspaces
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DOI：
10.1007/978-0-85729-932-1_2
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Gregory Shakhnarovich;B. Moghaddam
中科院分区：
文献类型：
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作者：
Gregory Shakhnarovich;B. Moghaddam
表示为高维像素阵列的面部图像通常属于本质上低维的流形。人脸识别和计算机视觉研究总体上已经见证了人们对利用这种观察并应用代数和统计工具来提取和分析底层流形的技术越来越感兴趣。在本章中，我们大致按时间顺序描述了识别、参数化和分析线性和非线性子空间的技术，从最初的特征脸技术到最近引入的用于概率相似性分析的贝叶斯方法。我们还讨论了其中一些技术的比较实验评估，以及与子空间方法应用于不同姿势、照明和表达相关的实际问题。
Images of faces, represented as high-dimensional pixel arrays, often belong to a manifold of intrinsically low dimension. Face recognition, and computer vision research in general, has witnessed a growing interest in techniques that capitalize on this observation and apply algebraic and statistical tools for extraction and analysis of the underlying manifold. In this chapter we describe in roughly chronologic order techniques that identify, parameterize, and analyze linear and nonlinear subspaces, from the original Eigenfaces technique to the recently introduced Bayesian method for probabilistic similarity analysis. We also discuss comparative experimental evaluation of some of these techniques as well as practical issues related to the application of subspace methods for varying pose, illumination, and expression.