Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class
Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class
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DOI：
10.1109/tpami.2002.1008382
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发表时间：
2002-06-01
影响因子：
23.6
通讯作者：
Martínez, AM
中科院分区：
文献类型：
--
作者：
Martínez, AM
尝试解决面部（或对象）识别问题的经典方法是使用大型和代表性的数据集。但是，在许多应用中，系统只有一个样本可供系统。在这项贡献中，我们描述了一种概率方法，即使每类只有一个单个训练样本，该系统也可以弥补不准确的本地化，部分遮挡和表达变化的面孔。为了解决本地化问题，我们找到了代表每个训练图像的此错误的子空间（例如特征空间，例如特征空间）。为了解决遮挡问题，每个面都分为k局部区域，这些区域被分离出来。与使用简单投票空间的其他方法相反，我们提出了一种概率方法，该方法分析了本地匹配的“良好”。为了使识别系统对训练上显示的面部表达和测试图像之间的差异较少敏感，我们根据该局部区域的数量受到在每个地方获得的结果的重量。当前的测试图像。
The classical way of attempting to solve the face (or object) recognition problem is by using large and representative data sets. In many applications, though, only one sample per class is available to the system. In this contribution, we describe a probabilistic approach that is able to compensate for imprecisely localized, partially occluded, and expression-variant faces even when only one single training sample per class is available to the system. To solve the localization problem, we find the subspace (within the feature space, e.g., eigenspace) that represents this error for each of the training images. To resolve the occlusion problem, each face is divided into k local regions which are analyzed in isolation. In contrast with other approaches where a simple voting space is used, we present a probabilistic method that analyzes how "good" a local match is. To make the recognition system less sensitive to the differences between the facial expression displayed on the training and the testing images, we weight the results obtained on each local area on the basis of how much of this local area is affected by the expression displayed on the current test image.