Face recognition by independent component analysis
Face recognition by independent component analysis
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DOI：
10.1109/tnn.2002.804287
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发表时间：
2002-11-01
影响因子：
--
通讯作者：
Sejnowski, TJ
中科院分区：
文献类型：
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作者：
Bartlett, MS;Movellan, JR;Sejnowski, TJ
许多当前的人脸识别算法使用由无监督统计方法发现的人脸表示。通常，这些方法找到一组基本图像，并将面部表示为这些图像的线性组合。主成分分析（PCA）是这种方法的一个流行的例子。PCA找到的基础图像仅取决于图像数据库中像素之间的成对关系。在诸如人脸识别这样的任务中，重要信息可能包含在像素之间的高阶关系中，似乎有理由期望可以通过对敏感的方法找到更好的基础图像。这些高阶统计量。独立分量分析（伊卡），PCA的推广，就是这样一种方法。我们使用了一种源自通过S形神经元进行最佳信息传递原理的伊卡版本。伊卡在FERET数据库中的人脸图像上进行了两种不同的架构，一种是将图像视为随机变量，将像素视为结果，另一种是将像素视为随机变量，将图像视为结果。第一个架构发现空间局部基础图像的脸。第二个架构产生了一个阶乘的面孔代码。这两种伊卡表示优于上级表示PCA的基础上，在几天内识别面部表情的变化。结合两种伊卡表示的分类器给出了最佳性能。
A number of current face recognition algorithms use face representations found by unsupervised statistical methods. Typically these methods find a set of basis images and represent faces as a linear combination of those images. Principal component analysis (PCA) is a popular example of such methods. The basis images found by PCA depend only on pairwise relationships between pixels in the image database. In a task such as face recognition, in which important information may be contained in the high-order relationships among pixels, it seems reasonable to expect that better basis images may be found by methods sensitive to. these high-order statistics. Independent component analysis (ICA), a generalization of PCA, is one such method. We used a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons. ICA was performed on face images in the FERET database under two different architectures, one which treated the images as random variables and the pixels as outcomes, and a second which treated the pixels as random variables and the images as outcomes. The first architecture found spatially local basis images for the faces. The second architecture produced a factorial face code. Both ICA representations were superior to representations based on PCA for recognizing faces across days and changes in expression. A classifier that combined the two ICA representations gave the best performance.