Beveridge J Ross, Draper Bruce A, Chang Jen-Mei, Kirby Michael, Kley Holger, Peterson Chris
Computer Science Department, Colorado State University, Fort Collins, CO 80523-1873, USA.
IEEE Trans Pattern Anal Mach Intell. 2009 Feb;31(2):351-63. doi: 10.1109/TPAMI.2008.200.
The theory of illumination subspaces is well developed and has been tested extensively on the Yale Face Database B (YDB) and CMU-PIE (PIE) data sets. This paper shows that if face recognition under varying illumination is cast as a problem of matching sets of images to sets of images, then the minimal principal angle between subspaces is sufficient to perfectly separate matching pairs of image sets from nonmatching pairs of image sets sampled from YDB and PIE. This is true even for subspaces estimated from as few as six images and when one of the subspaces is estimated from as few as three images if the second subspace is estimated from a larger set (10 or more). This suggests that variation under illumination may be thought of as useful discriminating information rather than unwanted noise.
光照子空间理论已经得到了充分发展，并在耶鲁人脸数据库B（YDB）和卡内基梅隆大学的PIE（CMU - PIE）数据集上进行了广泛测试。本文表明，如果将不同光照条件下的人脸识别视为图像集与图像集匹配的问题，那么子空间之间的最小主角度足以将从YDB和PIE中采样的匹配图像集对与不匹配图像集对完美分开。即使对于从少至六幅图像估计得到的子空间，以及当其中一个子空间从少至三幅图像估计得到而第二个子空间从更大的集合（10幅或更多）估计得到时，情况也是如此。这表明光照变化可以被视为有用的区分信息，而不是不需要的噪声。
