In this work, we propose a novel learning-based face hallucination framework built in the DCT domain, which can produce a high-resolution face image from a single low-resolution one. The problem is formulated as inferring the DCT coefficients in frequency domain instead of estimating pixel intensities in spatial domain. Our study shows that DC coefficients can be estimated fairly accurately by simple interpolation-based methods. AC coefficients, which contain the information of local features of face image, cannot be estimated well using interpolation. A simple but effective learning-based inference model is proposed to infer the AC coefficients. Experiments have been conducted to demonstrate the effectiveness of the proposed method in producing high quality hallucinated face images.