It is well known that the effect of illumination is mainly on the large-scale features (low-frequency components) of a face image. In solving the illumination problem for face recognition, most (if not all) existing methods either only use extracted small-scale features while discard large-scale features, or perform normalization on the whole image. In the latter case, small-scale features may be distorted when the large-scale features are modified. In this paper, we argue that large-scale features of face image are important and contain useful information for face recognition as well as visual quality of normalized image. Moreover, this paper suggests that illumination normalization should mainly perform on large-scale features of face image rather than the whole face image. Along this line, a novel framework for face illumination normalization is proposed. In this framework, a single face image is first decomposed into large- and small- scale feature images using logarithmic total variation (LTV) model. After that, illumination normalization is performed on large-scale feature image while small-scale feature image is smoothed. Finally, a normalized face image is generated by combination of the normalized large-scale feature image and smoothed small-scale feature image. CMU PIE and (Extended) YaleB face databases with different illumination variations are used for evaluation and the experimental results show that the proposed method outperforms existing methods.