This work proposes a structured random projection via feature weighting for cancelable identity verification. Essentially, projected facial features are weighted based on their discrimination capability prior to a matching process. In order to conceal the face identity, an averaging over several templates with different transformations is performed. Finally, several cancelable templates extracted from partial face images are fused at score level via a total error rate minimization. Our empirical experiments on two experimental scenarios using AR, FERET and Sheffield databases show that the proposed method consistently outperforms competing state-of-the-art un-supervised methods in terms of verification accuracy.