光照变化条件下的人脸识别技术研究

为了提高光照变化条件下的人脸识别率，针对Retinex算法处理人脸光照图像时易产生“光晕”难题，提出了一种基于Mean-Shift滤波的Retinex算法，并应用于人脸识别中的光照预处理。对人脸图像进行非线性增强；利用Mean-Shift滤波代替高斯滤波对光照估计，解决传统Retinex算法中存在的“光晕”难题。采用Yale B人脸库对算法性能进行测试，结果表明，该算法能够很好地抑制“光晕”现象的发生，具有光照鲁棒性，提高了人脸的识别率。

Research on face recognition under illumination variations

In order to improve the face recognition rate under illumination variations, this paper proposes an improved retinex algorithm which Gauss filter is replaced by mean-shift filter to solve“halo”phenomenon in traditional retinex algo-rithm for face illumination image. The face image is enhanced by nonlinear method, and then mean-shift filter is used to estimate the illumination instead of gauss filter to solve the traditional retinex algorithm’s“halo”phenomenon problem. The algorithm’s performance is test by Yale B face database, the results show that the proposed algorithm can restrain the“halo”phenomenon and has illumination robustness to improve the face recognition rate.