基于球面谐波基图像的任意光照下的人脸识别

提出了一种基于球面谐波基图像的光照补偿算法,用以在任意光照条件下进行人脸识别.算法分两步进行：光照估计和光照补偿.基于人脸形状大致相同和每个人脸的反射率基本相等的假设,首先估计了输入人脸图像光照的9个低频谐波系数.根据光照估计的结果,提出了两种光照补偿方法：纹理图像和差图像.纹理图像为输入图像与其光照辐照图之商,与输入图像的光照条件无关.差图像为输入图像与平均人脸在相同光照下的图像之差,通过减去平均人脸在相同光照下的图像,减弱了光照的影响.在CMU-PIE人脸库和Yale B人脸库上的实验表明,通过光照补偿,不同光照下人脸图像识别率有了很大提高.

Face Recognition under Varying Lighting Based on the Harmonic Images

The performances of the current face recognition systems suffer heavily from the variations in lighting. To deal with this problem, this paper presents an illumination normalization approach based on the harmonic images model. There are two steps in the algorithm, illumination estimation and illumination normalization. Benefiting from the observations that human faces share similar shape, and the albedos of the face surfaces are quasi-constant, the authors first estimate the nine coefficients of the low-frequency components of the illumination. Then the authors compensate the effect of the illumination by defining two canonical images, the texture image and the difference image. The texture image is defined as the ratio between the input image and its Jrradiance and it is illumination invariant. The difference image is defined as the difference between the input image and the image of the average face model under the same illumination. Therefore the effect of the illumination is weakened and the valuable information for recognition is still en- coded in the difference image. The experiments on the CMU-PIE face database and the Yale B face database have shown that the proposed method improve the performance of a face recognition system significantly when the probes are collected under varying lighting conditions.