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.