Face recognition under complex illumination conditions is still an open question. To cope with the problem, this paper proposes an effective illumination normalization method. The proposed method employs morphology and quotient image techniques by analyzing the face illumination, and it is upgraded with dynamical lighting estimation technique to strengthen illumination compensation and feature enhancement. Compared with traditional approaches, this method doesn't need any training data and any assumption on the light conditions, moreover, the enrollment requires only one image for each subject. The proposed methods are evaluated on Yale Face database B and receive a very competitive recognition rate with low computational cost.%复杂光照条件下的人脸识别是一个困难但需迫切解决的问题,为此提出了一种有效的光照归一化算法.该方法根据面部光照特点,基于数学形态学和商图像技术对各种光照条件下的人脸图像进行归一化处理,并且将它发展到动态地估计光照强度,进一步增强消除光照和保留特征的效果.与传统的技术相比,该方法无须训练数据集以及假定光源位置,并且每人只需一幅注册图像.在耶鲁人脸图像库B上的测试表明,该算法以较小的计算代价取得了优良的识别性能.
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