一种人脸标准光照图像的线性重构方法

A Linear Reconstruction Method for Face Images under Normal Illumination

基于相同光照下不同人脸图像与其标准光照图像之间的稳定关系,文中提出一种人脸标准光照图像重构方法.首先,为消除人脸结构影响,引入人脸三维变形,实现图像像素级对齐.其次,根据图像明暗变化,给出一种基于图像分块的光照分类方法.最后,对于形状对齐后的不同光照类别样本,训练出基于子空间的线性重构模型.该方法有效避免传统预处理方法带来的重构图像纹理丢失和子空间方法引起的图像失真.Extended Yale B数据库上实验表明,该方法对图像真实度与人脸识别率的提升,也验证文中人脸对齐和光照分类方法的有效性.

Based on the stable relationships between the face representations under the certain and the normal illumination for different individuals, an approach to reconstruct face images under normal illumination is proposed. Firstly, to eliminate the impact of facial surfaces, an image deformation method in 3D domain is applied to achieve pixel-level alignment. Then, an illumination classification method based on image blocking is proposed to classify the images with the same lighting gradation. Finally, various linear reconstruction models of different illumination categories based on facial subspaces are trained from the preprocessing image pairs for face image reconstruction. The method effectively avoids the loss of the facial texture in image preprocessing and the distortion in image subspace. The experimental results of the proposed method on Extended Yale B demonstrate the performance in image representation and face recognition and verify the effectiveness in face alignment and illumination classification.