We present a novel technique for the problem of super-resolution of facial data. The method uses a patch-based technique, and for each low-resolution input image patch, we seek the best matching patches from a database of face images using the Coherency Sensitive Hashing technique. Coherency Sensitive Hashing relies on hashing to combine image coherence cues and image appearance cues to effectively find matching patches in images. This differs from existing methods that apply a high-pass filter on input patches to extract local features. We then perform a weighted sum of the best matching patches to get the enhanced image. We compare with state-of-the-art techniques and observe that the approach provides better performance in terms of both visual quality and reconstruction error.