In this paper, we propose a novel regularized sparse coding approach for template-based unconstrained face verification. Unlike traditional verification tasks, which require the evaluation on image-to-image or video-to-video pairs, template-based face verification/recognition methods can exploit training and/or gallery data containing a mixture of both images or videos from the person of interest. The proposed regularized sparse coding approach addresses the adaptation to training and gallery data using three steps. First, we construct a reference dictionary, which represents the training set. Then we learn the discriminative sparse codes of the templates for verification through the proposed template regularized sparse coding approach. Finally, we measure the similarity between templates. An efficient algorithm is employed to learn the template regularized sparse codes. Extensive experiments on the template-based verification benchmark dataset show that the proposed approach outperforms several state-of-the-art methods.