任意光照下人脸图像的低维光照空间表示

本文提出一种不同光照条件下人脸图像的低维光照空间表示方法.这种低维光照空间表示不仅能够由输入图像估计其光照参数，而且能够由给定的光照条件生成虚拟的人脸图像.利用主成分分析和最近邻聚类方法得到9个基本点光源的位置，这9个基本点光源可以近似人脸识别应用中几乎所有的光照条件.在这9个基本光源照射下的9幅人脸基图像构成了低维人脸光照空间，它可以表示不同光照条件下的人脸图像，结合光照比图像方法，可以生成不同光照下的虚拟人脸图像.本文提出的低维光照空间的最大优点是利用某个人脸的图像建立的光照空间，可以用于不同的人脸.图像重构和不同光照下的人脸识别实验说明了本文算法的有效性.

A low-dimensional illumination space representation of human faces for arbitrary lighting conditions

The proposed method for low-dimensional illumination space representation (LDISR) of human faces can not only synthesize a virtual face image when given lighting conditions but also estimate lighting conditions when given a face image. The LDISR is based on the observation that 9 basis point light sources can represent almost arbitrary lighting conditions for face recognition application and different human faces have a similar LDISR. The principal component analysis (PCA) and the nearest neighbor clustering method are adopted to obtain the 9 basis point light sources. The 9 basis images under the 9 basis point light sources are then used to construct an LDISR which can represent almost all face images under arbitrary lighting conditions. Illumination ratio image (IRI) is employed to generate virtual face images under different illuminations. The LDISR obtained from face images of one person can be used for other people. Experimental results on image reconstruction and face recognition indicate the efficiency of LDISR.