Face recognition under varying illumination remains a challenging problem. Much progress has been made toward a solution through methods that require multiple gallery images of each subject under varying illumination. Yet for many applications, this requirement is too severe. In this paper, we propose a novel method that requires only a single gallery image per subject taken under unknown lighting. The method builds upon two contributions. We first estimate the lighting from its reflection in the eyes. This allows us to explicitly recover the illumination in the single gallery images as well as the probe image. Next, we exploit the local linearity of face appearance variation across different people. We represent the gallery images as locally linear montages of images of many different faces taken under the same lighting (bootstrap images). Then, we transfer the estimated combination of bootstrap images to synthesize each subject?s face under the probe lighting to accomplish recognition. Finally, we show through tests on the CMU PIE database that we can achieve better recognition results using our lighting estimation method and locally linear montages than the current state-of-the-art.