基于微观本义复原的人脸图像光照正规化

为解决变光照下人脸识别的识别率低,光照正规化算法复杂.不易实现的问题,提出一个新的光照正规化方法一微观本义复原,即通过微观邻域上像素本义特征的定义,将整体图像上变光照下的非线性灰度变化转化为微观邻域内的线性变化,一定程度上避免了包括图像自身结构在内的不确定因素对图像复原的不利影响;并以邻域内的本义特征为光照不敏感特征,对本义特征进行结构编码,并用最小二乘法拟合编码值与光照方向之间的关系,最后根据得到的光照正规化参数复原图像.实验结果表明,该方法算法简单,易于实现,能适应实时的人脸识别系统,在光照变化90.以内的Yale B库的平均识别率可达94.1%.

Illumination Normalization for Face Recognition Based on Inherent Texture Features within Micro-Neighborhood

Face recognition under varying illumination is difficult, and we propose a new illumination normalization method based on inherent texture features within micro-neighborhood, which transforms the nonlinear process of grey level changes with illumination into a linear process in the micro-neighborhood. So, some harmful influence on image recovery by uncertainties including that from image structure is avoided. The inherent texture future is illumination insensitive, the structure of which is coded. The least squares estimation is used to estimate the relationship of grey level changes at certain illumination direction and the coded features, and illumination normalization is then performed according to the estimated relations. The computational complexity of this algorithm is low. The experimental results show that by implementing the algorithm in face recognition system, we can achieve an average recognition ratio of 94.1% on Yale B database with 90° illumination change.