text: Face hallucination is a generative task to super-resolve the facial image with low resolution while human perception of face heavily relies on identity information. However, previous face hallucination approaches largely ignore facial identity recovery. This paper proposes Super-Identity Convolutional Neural Network (SICNN) to recover identity information for generating faces closed to the real identity. Specifically, we define a super-identity loss to measure the identity difference between a hallucinated face and its corresponding high-resolution face within the hypersphere identity metric space. However, directly using this loss will lead to a Dynamic Domain Divergence problem, which is caused by the large margin between the high-resolution domain and the hallucination domain. To overcome this challenge, we present a domain-integrated training approach by constructing a robust identity metric for faces from these two domains. Extensive experimental evaluations demonstrate that the proposed SICNN achieves superior visual quality over the state-of-the-art methods on a challenging task to super-resolve 12 ( ) 14 faces with an 8 ( ) upscaling factor. In addition, SICNN significantly improves the recognizability of ultra-low-resolution faces.
  @cite_41: A novel face hallucination method is proposed in this paper for the reconstruction of a high-resolution face image from a low-resolution observation based on a set of high- and low-resolution training image pairs. Different from most of the established methods based on probabilistic or manifold learning models, the proposed method hallucinates the high-resolution image patch using the same position image patches of each training image. The optimal weights of the training image position-patches are estimated and the hallucinated patches are reconstructed using the same weights. The final high-resolution facial image is formed by integrating the hallucinated patches. The necessity of two-step framework or residue compensation and the differences between hallucination based on patch and global image are discussed. Experiments show that the proposed method without residue compensation generates higher-quality images and costs less computational time than some recent face image super-resolution (hallucination) techniques.
  @cite_27: In this paper, we study face hallucination, or synthesizing a high-resolution face image from an input low-resolution image, with the help of a large collection of other high-resolution face images. Our theoretical contribution is a two-step statistical modeling approach that integrates both a global parametric model and a local nonparametric model. At the first step, we derive a global linear model to learn the relationship between the high-resolution face images and their smoothed and down-sampled lower resolution ones. At the second step, we model the residue between an original high-resolution image and the reconstructed high-resolution image after applying the learned linear model by a patch-based non-parametric Markov network to capture the high-frequency content. By integrating both global and local models, we can generate photorealistic face images. A practical contribution is a robust warping algorithm to align the low-resolution face images to obtain good hallucination results. The effectiveness of our approach is demonstrated by extensive experiments generating high-quality hallucinated face images from low-resolution input with no manual alignment.
  @cite_42: In this paper, we propose a face-hallucination method, namely face hallucination based on sparse local-pixel structure. In our framework, a high resolution (HR) face is estimated from a single frame low resolution (LR) face with the help of the facial dataset. Unlike many existing face-hallucination methods such as the from local-pixel structure to global image super-resolution method (LPS-GIS) and the super-resolution through neighbor embedding, where the prior models are learned by employing the least-square methods, our framework aims to shape the prior model using sparse representation. Then this learned prior model is employed to guide the reconstruction process. Experiments show that our framework is very flexible, and achieves a competitive or even superior performance in terms of both reconstruction error and visual quality. Our method still exhibits an impressive ability to generate plausible HR facial images based on their sparse local structures. Our framework aims to shape the prior model using sparse representation.Global structure and local-pixel structure are incorporated to produce plausible facial details.A method to learn local-pixel structures based on sparse representation is proposed.The proposed method is competitive with other, state-of-the-art face-hallucination methods.
summary: For subspace-based methods. @cite_27 employed a Principal Component Analysis (PCA) based global appearance model to hallucinate LR faces and a local non-parametric model to enhance the details. @cite_41 used multiple local exemplar patches sampled from aligned HR facial images to hallucinate LR faces. @cite_42 resolved to sparse representation on local face patches. These subspace-based methods require precisely aligned reference HR and LR facial images with the same pose and facial expression.