中文说明：创建图像库！这样一来，由于人脸结构的相似性，一旦把这些人脸图像归一化之后，就可以明显的看出，这些人脸图像在高维空间中并不是随机和散乱分布的，而是存在一种规律。在接下来的实验当中，N取128沃们知道，K-L变换方法从本质上来说，就是一种由人脸图像的像素点的灰度级在空间分布时候给你的相关性来决定的。所以，在进行K-L变换之前，沃们需要对人脸图像进行相应的一系列预先处理，以达到位置的校准和灰度归一化的目的。那么接下来沃们先看看图像的归一化，在后面沃们会把归一化的图像应用在K-L变换当中。
English Description：
Create an image gallery! In this way, due to the similarity of facial structures, once normalized after the facial image, you can plainly see, the facial image is not random and scattered distribution in a higher dimensional space, but the existence of a law. In the next experiment, n is 128Us know, k-l transform method in essence, is a facial image pixels in gray-scale spatial correlation of time for you to decide. Therefore, prior to the k-l transformation, us need to face a series of treatment in order to achieve the purpose of calibration and gray normalized for location. Then us first look at the image of normalization, behind us image applied to the normalized k-l transform.