Representations for Recognition Under Variable Illumination
Representations for Recognition Under Variable Illumination
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可变照明下的识别表示
DOI：
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发表时间：
1999
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通讯作者：
A. Georghiades
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作者：
D. Kriegman;P. Belhumeur;A. Georghiades
由于照明的可变性，即使在固定的姿势下观察，同一个物体也会出现显著的不同。因此，一个对象识别系统必须采用一个表示，要么是不变的，或模型的变化。本章介绍了一种基于外观的方法来建模这种变化。特别是，我们证明了一组的n-像素的单色图像的凸对象的朗伯反射函数，照亮任意数量的点光源在无穷远，形成一个凸多面体锥在Rn和这个照明锥的尺寸等于不同的表面法线的数量。对于具有更一般反射函数的非凸对象，图像集也是凸锥。被认为是单色和彩色相机的这些锥体的几何特性。在这里，提出了一种方法，当表面是连续的，可能是非凸的和朗伯的时，从少量的图像中构造圆锥体表示;这说明了附着和投射的阴影。对于一组物体，每个物体用一个圆锥体表示，通过测量图像到每个圆锥体的最小距离，通过最近邻分类进行识别。我们证明了这种方法的实用性的问题，人脸识别（一类非凸和非朗伯物体具有相似的几何形状）。该方法在10张人脸的660幅图像数据库上进行了测试，结果超过了现有的流行方法。
Due to illumination variability, the same object can appear dramatically different even when viewed in fixed pose. Consequently, an object recognition system must employ a representation that is either invariant to, or models this variability. This chapter presents an appearance-based method for modeling this variability. In particular, we prove that the set of n-pixel monochrome images of a convex object with a Lambertian reflectance function, illuminated by an arbitrary number of point light sources at infinity, forms a convex polyhedral cone in Rn and that the dimension of this illumination cone equals the number of distinct surface normals. For a non-convex object with a more general reflectance function, the set of images is also a convex cone. Geometric properties of these cones for monochrome and color cameras are considered. Here, present a method for constructing a cone representation from a small number of images when the surface is continuous, possibly non-convex, and Lambertian; this accounts for both attached and cast shadows. For a collection of objects, each object is represented by a cone, and recognition is performed through nearest neighbor classification by measuring the minimal distance of an image to each cone. We demonstrate the utility of this approach to the problem of face recognition (a class of non-convex and non-Lambertian objects with similar geometry). The method is tested on a database of 660 images of 10 faces, and the results exceed those of popular existing methods.