利用改进的Retinex进行人脸图像光照处理

Face image illumination processing based on improved Retinex

为了提高光照变化条件下的人脸识别率,针对Retinex算法处理人脸侧光照图像时会误增强阴影的边缘提出了一种基于新的传导函数的自适应平滑Retinex算法。该传导函数用空间梯度和像素的局部不一致性两种方式共同测量灰度的剧烈变化,在平滑图像的同时没有边缘增强效应,且不会损失人脸特征边缘。在平滑估计的迭代过程中,选取上一次与此次迭代结果中的较大值作为约束条件来保证估计出的亮度图像能满足Retinex理论的约束条件。在YaleB人脸库上的实验结果表明,本文算法能有效克服强侧光照时的阴影现象且没有损失人脸特征边缘;与较经典Retinex算法相比,侧光照时的识别率在最好情况时提高了24.2%,无强侧光照时也可提高4%左右,具有光照鲁棒性,可适用于任何光照条件下的人脸识别。

Illumination variation is one of the most significant factors affecting the performance of face recognition systems.As the adaptive smoothing estimation used in traditional Retinex algorithm can enhance the shadow edge falsely during smoothing face images,a novel Retinex algorithm based on adaptive smoothing with new a conduction function is proposed.This conduction function uses both spatial gradient and local inhomogeneity to measure the severity extent of pixel variation,which can smooth face images well without enhancing edge effects and lossing feature edges.During iterative process,the maximum between this iteration and last iteration is chosen to be the constraint,so this adaptive smoothing method with the proposed conduction function can be applied in Retinex theory to estimate illumination.Experimental results based on the Yale B face database show that the proposed algorithm can overcome shadows in side-illumination efficiently without losing feature edges of face images.The recognition rate is improved by 24.2% in the side-illumination condition at the best rate,and 4% in the no side-illumination condition compared with those of classical Retinex algorithms.The experimental results prove that the algorithm has illumination robustness,and can improve recognition rates under any illumination conditions effectively.