Hallucinating faces based on adaptive neighborhood selection and dual tree complex wavelet transform

In this paper, a novel face hallucination method is proposed. Instead of utilizing the gray values of pixels directly, the feature vector extracted using dual tree complex wavelet transform (DTCWT) is exploited to describe the directional detailed information of facial image. Then, the adaptive neighborhood selection (ANS) algorithm is used to preserve neighborhood relationship between the low-resolution and high-resolution face by selecting the optimal neighbors of each facial patch from the feature space. The selected neighborhood can well reflect the local geometrical structure of face manifold, so that the linear subspace determined by the optimal linear fitting can approximate the local facial geometry with a higher accuracy. In order to reduce the effect of misalignment, a face alignment method based on three facial keypoints is also used. Experimental results show that the proposed face hallucination method outperforms other state-of-the-art methods in terms of visual inspection and objective evaluations, especially when the facial images are compressed and have low-resolution.