In this study, our goal is to verify identity of two facial images that captured under different scenarios (e.g., occluded facial image vs. non-occluded one). We introduce an example-based approach for face verification, i.e. the occluded facial image is recovered first, and then used for face verification process. In the synthesis module, training examples are used to provide a prior knowledge for the unseen occluded image reconstruction. In order to make sure the quality of recovered results, a verification step is embedded. Such discriminative-driven synthesis framework provides more reliable facial appearance for both face reconstruction and verification. According to the experimental results, the proposed system makes the synthesis results preserve the personal characteristics of system input.