Surveillance face hallucination via variable selection and manifold learning

In this paper, we propose a new two-step face hallucination method to induce a high-resolution (HR) face image from a low-resolution (LR) observation. Especially for low-quality surveillance face image, an RBF-PLS based variable selection method is presented for the reconstruction of global face image. Further more, in order to compensate for the reconstruction errors, which are lost high frequency detailed face features, the Neighbor Embedding (NE) based residue face hallucination algorithm is used. Compared with current methods, the proposed RBF-PLS based method can generate a global face more similar to the original face and less sensitive to noise, moreover, the NE algorithm can reduce the reconstruction errors caused by misalignment on the basis of a carefully designed search strategy. Experiments show the superiority of the proposed method compared with some state-of-the-art approaches and the efficacy both in simulation and real surveillance condition.