Probabilistic Linear Discriminant Analysis for Inferences About Identity
Probabilistic Linear Discriminant Analysis for Inferences About Identity
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DOI：
10.1109/iccv.2007.4409052
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发表时间：
2007-12
期刊：
影响因子：
--
通讯作者：
S. Prince;J. Elder
中科院分区：
文献类型：
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作者：
S. Prince;J. Elder
当探针和画廊图像的照明或姿势不同时，许多当前的面部识别算法的表现不佳。在本文中，我们提出了一种针对这些条件的新型算法。我们将面部数据描述为来自生成模型的产生，该模型既包含了个体内部和个体之间的变化。在认可中，我们计算出面部图像之间的差异完全是由于个体内的可变性所致。我们将其扩展到可以描述任意面部歧管的非线性情况，并且噪声依赖于位置。我们还开发了该算法的“绑定”版本，该版本允许在截然不同的观看条件下进行明确比较。我们证明，我们的模型在不同姿势下的面部识别（ii）面部识别（ii）面部识别产生了最先进的结果。
Many current face recognition algorithms perform badly when the lighting or pose of the probe and gallery images differ. In this paper we present a novel algorithm designed for these conditions. We describe face data as resulting from a generative model which incorporates both within-individual and between-individual variation. In recognition we calculate the likelihood that the differences between face images are entirely due to within-individual variability. We extend this to the non-linear case where an arbitrary face manifold can be described and noise is position-dependent. We also develop a "tied" version of the algorithm that allows explicit comparison across quite different viewing conditions. We demonstrate that our model produces state of the art results for (i) frontal face recognition (ii) face recognition under varying pose.