Evaluations of the state-of-the-art of both academic face recognition algorithms and commercial systems have shown that recognition performance of most current technologies degrades due to the variations of illumination. This paper investigates several illumination normalization methods and proposes some novel solutions. The main contribution of this paper includes: (1) A Gamma Intensity Correction (GIC) method is proposed to normalize the overall image intensity at the given illumination level; (2) A Region-based strategy combining GIC and the Histogram Equalization (HE) is proposed to further eliminate the side-lighting effect; (3) A Quotient Illumination Relighting (QIR) method is presented to synthesize images under a pre-defined normal lighting condition from the provided face images captured under non-normal lighting condition. These methods are evaluated and compared on the Yale illumination face database B and Harvard illumination face database. Considerable improvements are observed. Some conclusions are given at last.