光照变化条件下的人脸识别研究

Face Recognition under Varying Illumination

本文提出了两种研究光照变化条件下人脸识别的方法。第1种方法是光照子空间方法,它适用于训练集中存在与测试人脸图像相同或者相似光照的人脸训练图像。当这个条件不满足时,可利用径向基函数网络产生虚拟光照条件下的人脸图像样本或图像特征加入训练集,该方法适用于更一般的情况。实验结果证明文中提出的方法可以有效提高识别率。

For tackling the problem of face recognition when illumination varied in direction, we proposed two face recognition algorithms. The first is illumination subspace method. We constructed different subspaces that correspond respectively to different illumination directions. We projected the test face image to the subspace having the same illumination direction and perform feature extraction. We then completed face recognition through feature matching between test image and the corresponding subspace. When applicable, illumination subspace method is quite effective. The second method is more general than the first. In the second method, we produced face images under virtual illumination, which is made possible through training RBFN (radial basis function network) with images whose illumination directions are known. Thus we can implement feature matching between test images under any illumination direction and produce virtual image having the same illumination direction. Experimental results show that the illumination subspace method achieves a recognition ratio higher than that achieved by the standard eigenface method. Experimental results also show that the produced face images under virtual illumination can be used effectively as training images without adversely affecting the recognition ratio.