From few to many: Illumination cone models for face recognition under variable lighting and pose
From few to many: Illumination cone models for face recognition under variable lighting and pose
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DOI：
10.1109/34.927464
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发表时间：
2001-06-01
影响因子：
23.6
通讯作者：
Kriegman, DJ
中科院分区：
文献类型：
--
作者：
Georghiades, AS;Belhumeur, PN;Kriegman, DJ
提出了一种基于生成外观的人脸识别方法，用于在光照和视角变化的情况下识别人脸。我们的方法利用了这样一个事实，即一个对象的图像集在固定的姿势，但在所有可能的照明条件下，是一个凸锥的图像空间。使用少量的训练图像的每个人脸采取不同的照明方向，人脸的形状和轮廓可以重建。反过来，这种重建作为一个生成模型，可用于渲染或合成的面部图像下的新姿态和照明条件。然后对姿势空间进行采样，并且对于每个姿势。通过低维线性子空间来近似对应的照明锥，使用生成模型来估计该低维线性子空间的基向量。我们的识别算法分配给一个测试图像的身份最接近的近似照明锥（基于图像空间内的欧几里得距离）。我们在来自耶鲁人脸数据库B的4,050张图像上测试了我们的人脸识别方法;这些图像包含10个人的405种观看条件（9个姿势x 45个照明条件）。该方法几乎没有错误，除了在最极端的照明方向，并显着优于流行的识别方法，不使用生成模型。
We present a generative appearance-based method for recognizing human faces under variation in lighting and viewpoint. Our method exploits the fact that the set of images of an object in fixed pose, but under all possible illumination conditions, is a convex cone in the space of images. Using a small number of training images of each face taken with different lighting directions, the shape and albedo of the face can be reconstructed. In turn, this reconstruction serves as a generative model that can be used to render-or synthesize-images of the face under novel poses and illumination conditions. The pose space is then sampled and, for each pose. the corresponding illumination cone is approximated by a low-dimensional linear subspace whose basis vectors are estimated using the generative model. Our recognition algorithm assigns to a test image the identity of the closest approximated illumination cone (based on Euclidean distance within the image space). We test our face recognition method on 4,050 images from the Yale Face Database B; these images contain 405 viewing conditions (9 poses x 45 illumination conditions) for 10 individuals. The method performs almost without error, except on the most extreme lighting directions, and significantly outperforms popular recognition methods that do not use a generative model.