基于光照分类的可变光照下人脸识别方法

针对人脸识别中的光照变化问题，借鉴“分而治之”的思想，提出通过光照分类来提高不同光照情况下人脸的识别率。根据人脸图像灰度随光照变化的分布特点，将图像划分为三类：无偏光类、左偏光类和右偏光类，分别在不同的光照子集中对人脸图像进行处理与识别，并在YALEB人脸库上完成实验验证。结果表明，该方法不需要进行光照归一化处理，有效减弱了光照不均匀对人脸识别的影响，在提高识别率的同时降低了运算量，识别率可从未分类前的86.7%提高到99.6%，对于可变光照下的人脸识别有一定的应用前景。

Lighting-variant face recognition based on illumination categorization

A novel approach based on illumination categorization is proposed to solve the problem of illumination variation in face recognition,learning from the thoughts of＂divide and conquer＂.According to the gray level distribution of faces,illumination is grouped into three categories：Non-polarized,left-polarized and right-polarized illumination subsets.Then image processing and face recognition are implemented respectively in each subset.The experimental results on YALEB face database show that the proposed approach which is no need for illumination normalization weakens the impact of uneven illumination on face recognition effectively,and the recognition accuracy is improved from 86.7% of pre-categorization to 99.6%,furthermore,the computational complexity is reduced.This method can be applied in face recognition under variable lighting.