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.
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.
@cite_41 @cite_27 @cite_42
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Given a tiny face image, existing face hallucination methods aim at super-resolving its high-resolution (HR) counterpart by learning a mapping from an exemplar dataset. Since a low-resolution (LR) input patch may correspond to many HR candidate patches, this ambiguity may lead to distorted HR facial details and wrong attributes such as gender reversal. An LR input contains low-frequency facial components of its HR version while its residual face image, defined as the difference between the HR ground-truth and interpolated LR images, contains the missing high-frequency facial details. We demonstrate that supplementing residual images or feature maps with additional facial attribute information can significantly reduce the ambiguity in face super-resolution. To explore this idea, we develop an attribute-embedded upsampling network, which consists of an upsampling network and a discriminative network. The upsampling network is composed of an autoencoder with skip-connections, which incorporates facial attribute vectors into the residual features of LR inputs at the bottleneck of the autoencoder and deconvolutional layers used for upsampling. The discriminative network is designed to examine whether super-resolved faces contain the desired attributes or not and then its loss is used for updating the upsampling network. In this manner, we can super-resolve tiny (16A—16 pixels) unaligned face images with a large upscaling factor of 8A— while reducing the uncertainty of one-to-many mappings remarkably. By conducting extensive evaluations on a large-scale dataset, we demonstrate that our method achieves superior face hallucination results and outperforms the state-of-the-art. 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. Face hallucination is a domain-specific super-resolution problem with the goal to generate high-resolution (HR) faces from low-resolution (LR) input images. In contrast to existing methods that often learn a single patch-to-patch mapping from LR to HR images and are regardless of the contextual interdependency between patches, we propose a novel Attention-aware Face Hallucination (Attention-FH) framework which resorts to deep reinforcement learning for sequentially discovering attended patches and then performing the facial part enhancement by fully exploiting the global interdependency of the image. Specifically, in each time step, the recurrent policy network is proposed to dynamically specify a new attended region by incorporating what happened in the past. The state (i.e., face hallucination result for the whole image) can thus be exploited and updated by the local enhancement network on the selected region. The Attention-FH approach jointly learns the recurrent policy network and local enhancement network through maximizing the long-term reward that reflects the hallucination performance over the whole image. Therefore, our proposed Attention-FH is capable of adaptively personalizing an optimal searching path for each face image according to its own characteristic. Extensive experiments show our approach significantly surpasses the state-of-the-arts on in-the-wild faces with large pose and illumination variations. In video surveillance, the captured face images are usually of low resolution (LR). Thus, a framework based on singular value decomposition (SVD) for performing both face hallucination and recognition simultaneously is proposed in this paper. Conventionally, LR face recognition is carried out by super-resolving the LR input face first, and then performing face recognition to identify the input face. By considering face hallucination and recognition simultaneously, the accuracy of both the hallucination and the recognition can be improved. In this paper, singular values are first proved to be effective for representing face images, and the singular values of a face image at different resolutions have approximately a linear relation. In our algorithm, each face image is represented using SVD. For each LR input face, the corresponding LR and high-resolution (HR) face-image pairs can then be selected from the face gallery. Based on these selected LR–HR pairs, the mapping functions for interpolating the two matrices in the SVD representation for the reconstruction of HR face images can be learned more accurately. Therefore, the final estimation of the high-frequency details of the HR face images will become more reliable and effective. The experimental results demonstrate that our proposed framework can achieve promising results for both face hallucination and recognition. Conventional face hallucination methods rely heavily on accurate alignment of low-resolution (LR) faces before upsampling them. Misalignment often leads to deficient results and unnatural artifacts for large upscaling factors. However, due to the diverse range of poses and different facial expressions, aligning an LR input image, in particular when it is tiny, is severely difficult. To overcome this challenge, here we present an end-to-end transformative discriminative neural network (TDN) devised for super-resolving unaligned and very small face images with an extreme upscaling factor of 8. Our method employs an upsampling network where we embed spatial transformation layers to allow local receptive fields to line-up with similar spatial supports. Furthermore, we incorporate a class-specific loss in our objective through a successive discriminative network to improve the alignment and upsampling performance with semantic information. Extensive experiments on large face datasets show that the proposed method significantly outperforms the state-of-the-art. For face recognition, image features are first extracted and then matched to those features in a gallery set. The amount of information and the effectiveness of the features used will determine the recognition performance. In this paper, we propose a novel face recognition approach using information about face images at higher and lower resolutions so as to enhance the information content of the features that are extracted and combined at different resolutions. As the features from different resolutions should closely correlate with each other, we employ the cascaded generalized canonical correlation analysis (GCCA) to fuse the information to form a single feature vector for face recognition. To improve the performance and efficiency, we also employ "Gabor-feature hallucination", which predicts the high-resolution (HR) Gabor features from the Gabor features of a face image directly by local linear regression. We also extend the algorithm to low-resolution (LR) face recognition, in which the medium-resolution (MR) and HR Gabor features of a LR input image are estimated directly. The LR Gabor features and the predicted MR and HR Gabor features are then fused using GCCA for LR face recognition. Our algorithm can avoid having to perform the interpolation super-resolution of face images and having to extract HR Gabor features. Experimental results show that the proposed methods have a superior recognition rate and are more efficient than traditional methods. A face recognition approach which combines images at different resolutions is proposed.A low-resolution face recognition algorithm based on fusing images at different resolutions is proposed.A method for feature hallucination is proposed. The goal of face hallucination is to generate high-resolution images with fidelity from low-resolution ones. In contrast to existing methods based on patch similarity or holistic constraints in the image space, we propose to exploit local image structures for face hallucination. Each face image is represented in terms of facial components, contours and smooth regions. The image structure is maintained via matching gradients in the reconstructed high-resolution output. For facial components, we align input images to generate accurate exemplars and transfer the high-frequency details for preserving structural consistency. For contours, we learn statistical priors to generate salient structures in the high-resolution images. A patch matching method is utilized on the smooth regions where the image gradients are preserved. Experimental results demonstrate that the proposed algorithm generates hallucinated face images with favorable quality and adaptability. Most of the conventional face hallucination methods assume the input image is sufficiently large and aligned, and all require the input image to be noise-free. Their performance degrades drastically if the input image is tiny, unaligned, and contaminated by noise. In this paper, we introduce a novel transformative discriminative autoencoder to 8X super-resolve unaligned noisy and tiny (16X16) low-resolution face images. In contrast to encoder-decoder based autoencoders, our method uses decoder-encoder-decoder networks. We first employ a transformative discriminative decoder network to upsample and denoise simultaneously. Then we use a transformative encoder network to project the intermediate HR faces to aligned and noise-free LR faces. Finally, we use the second decoder to generate hallucinated HR images. Our extensive evaluations on a very large face dataset show that our method achieves superior hallucination results and outperforms the state-of-the-art by a large margin of 1.82dB PSNR. The neighbor-embedding (NE) algorithm for single-image super-resolution (SR) reconstruction assumes that the feature spaces of low-resolution (LR) and high-resolution (HR) patches are locally isometric. However, this is not true for SR because of one-to-many mappings between LR and HR patches. To overcome or at least to reduce the problem for NE-based SR reconstruction, we apply a joint learning technique to train two projection matrices simultaneously and to map the original LR and HR feature spaces onto a unified feature subspace. Subsequently, the k -nearest neighbor selection of the input LR image patches is conducted in the unified feature subspace to estimate the reconstruction weights. To handle a large number of samples, joint learning locally exploits a coupled constraint by linking the LR-HR counterparts together with the K-nearest grouping patch pairs. In order to refine further the initial SR estimate, we impose a global reconstruction constraint on the SR outcome based on the maximum a posteriori framework. Preliminary experiments suggest that the proposed algorithm outperforms NE-related baselines. Face Super-Resolution (SR) is a domain-specific superresolution problem. The facial prior knowledge can be leveraged to better super-resolve face images. We present a novel deep end-to-end trainable Face Super-Resolution Network (FSRNet), which makes use of the geometry prior, i.e., facial landmark heatmaps and parsing maps, to super-resolve very low-resolution (LR) face images without well-aligned requirement. Specifically, we first construct a coarse SR network to recover a coarse high-resolution (HR) image. Then, the coarse HR image is sent to two branches: a fine SR encoder and a prior information estimation network, which extracts the image features, and estimates landmark heatmaps parsing maps respectively. Both image features and prior information are sent to a fine SR decoder to recover the HR image. To generate realistic faces, we also propose the Face Super-Resolution Generative Adversarial Network (FSRGAN) to incorporate the adversarial loss into FSRNet. Further, we introduce two related tasks, face alignment and parsing, as the new evaluation metrics for face SR, which address the inconsistency of classic metrics w.r.t. visual perception. Extensive experiments show that FSRNet and FSRGAN significantly outperforms state of the arts for very LR face SR, both quantitatively and qualitatively. A two-phase face hallucination approach is proposed in this paper to infer high-resolution face image from the low-resolution observation based on a set of training image pairs. The proposed locality preserving hallucination (LPH) algorithm combines locality preserving projection (LPP) and radial basis function (RBF) regression together to hallucinate the global high-resolution face. Furthermore, in order to compensate the inferred global face with detailed inartificial facial features, the neighbor reconstruction based face residue hallucination is used. Compared with existing approaches, the proposed LPH algorithm can generate global face more similar to the ground truth face efficiently, moreover, the patch structure and search strategy carefully designed for the neighbor reconstruction algorithm greatly reduce the computational complexity without diminishing the quality of high-resolution face detail. The details of synthetic high-resolution face are further improved by a global linear smoother. Experiments indicate that our approach can synthesize distinct high-resolution faces with various facial appearances such as facial expressions, eyeglasses efficiently. In this letter, we propose a novel approach for learning coupled mappings to improve the performance of low-resolution (LR) face image recognition. The coupled mappings aim to project the LR probe images and high-resolution (HR) gallery images into a unified latent subspace, which is efficient to measure the similarity of face images with different resolutions. In the training phase, we first construct local optimization for each training sample according to the relationship of neighboring data points. The local optimization aims to: (1) ensure the consistency for each LR face image and corresponding HR one; (2) model the intrinsic geometric structure between each given sample and its neighbors; and (3) preserve the discriminative information across different subjects. We finally incorporate the local optimizations together for building the global structure. The coupled mappings can be learned by solving a standard eigen-decomposition problem, which avoids the small-sample-size problem. Experimental results demonstrate the effectiveness of the proposed method on public face databases. Abstract Face images captured by surveillance cameras usually have low-resolution (LR) in addition to uncontrolled poses and illumination conditions, all of which adversely affect the performance of face matching algorithms. In this paper, we develop a novel method to address the problem of matching a LR or poor quality face image to a gallery of high-resolution (HR) face images. In recent years, extensive efforts have been made on LR face recognition (FR) research. Previous research has focused on introducing a learning based super-resolution (LBSR) method before matching or transforming LR and HR faces into a unified feature space (UFS) for matching. To identify LR faces, we present a method called coupled discriminant multi-manifold analysis (CDMMA). In CDMMA, we first explore the neighborhood information as well as local geometric structure of the multi-manifold space spanned by the samples. And then, we explicitly learn two mappings to project LR and HR faces to a unified discriminative feature space (UDFS) through a supervised manner, where the discriminative information is maximized for classification. After that, the conventional classification method is applied in the CDMMA for final identification. Experimental results conducted on two standard face recognition databases demonstrate the superiority of the proposed CDMMA. 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. A deep neural network (DNN)-based new pan-sharpening method for the remote sensing image fusion problem is proposed in this letter. Research on representation learning suggests that the DNN can effectively model complex relationships between variables via the composition of several levels of nonlinearity. Inspired by this observation, a modified sparse denoising autoencoder (MSDA) algorithm is proposed to train the relationship between high-resolution (HR) and low-resolution (LR) image patches, which can be represented by the DNN. The HR LR image patches only sample from the HR LR panchromatic (PAN) images at hand, respectively, without requiring other training images. By connecting a series of MSDAs, we obtain a stacked MSDA (S-MSDA), which can effectively pretrain the DNN. Moreover, in order to better train the DNN, the entire DNN is again trained by a back-propagation algorithm after pretraining. Finally, assuming that the relationship between HR LR multispectral (MS) image patches is the same as that between HR LR PAN image patches, the HR MS image will be reconstructed from the observed LR MS image using the trained DNN. Comparative experimental results with several quality assessment indexes show that the proposed method outperforms other pan-sharpening methods in terms of visual perception and numerical measures. We propose an appearance-based face recognition method called the Laplacianface approach. By using locality preserving projections (LPP), the face images are mapped into a face subspace for analysis. Different from principal component analysis (PCA) and linear discriminant analysis (LDA) which effectively see only the Euclidean structure of face space, LPP finds an embedding that preserves local information, and obtains a face subspace that best detects the essential face manifold structure. The Laplacianfaces are the optimal linear approximations to the eigenfunctions of the Laplace Beltrami operator on the face manifold. In this way, the unwanted variations resulting from changes in lighting, facial expression, and pose may be eliminated or reduced. Theoretical analysis shows that PCA, LDA, and LPP can be obtained from different graph models. We compare the proposed Laplacianface approach with Eigenface and Fisherface methods on three different face data sets. Experimental results suggest that the proposed Laplacianface approach provides a better representation and achieves lower error rates in face recognition. The Reference-based Super-resolution (RefSR) super-resolves a low-resolution (LR) image given an external high-resolution (HR) reference image, where the reference image and LR image share similar viewpoint but with significant resolution gap ( (8 )). Existing RefSR methods work in a cascaded way such as patch matching followed by synthesis pipeline with two independently defined objective functions, leading to the inter-patch misalignment, grid effect and inefficient optimization. To resolve these issues, we present CrossNet, an end-to-end and fully-convolutional deep neural network using cross-scale warping. Our network contains image encoders, cross-scale warping layers, and fusion decoder: the encoder serves to extract multi-scale features from both the LR and the reference images; the cross-scale warping layers spatially aligns the reference feature map with the LR feature map; the decoder finally aggregates feature maps from both domains to synthesize the HR output. Using cross-scale warping, our network is able to perform spatial alignment at pixel-level in an end-to-end fashion, which improves the existing schemes [1, 2] both in precision (around 2 dB–4 dB) and efficiency (more than 100 times faster). This paper describes a single-image super-resolution (SR) algorithm based on nonnegative neighbor embedding. It belongs to the family of single-image example-based SR algorithms, since it uses a dictionary of low resolution (LR) and high resolution (HR) trained patch pairs to infer the unknown HR details. Each LR feature vector in the input image is expressed as the weighted combination of its K nearest neighbors in the dictionary; the corresponding HR feature vector is reconstructed under the assumption that the local LR embedding is preserved. Three key aspects are introduced in order to build a low-complexity competitive algorithm: (i) a compact but efficient representation of the patches (feature representation) (ii) an accurate estimation of the patches by their nearest neighbors (weight computation) (iii) a compact and already built (therefore external) dictionary, which allows a one-step upscaling. The neighbor embedding SR algorithm so designed is shown to give good visual results, comparable to other state-of-the-art methods, while presenting an appreciable reduction of the computational time. This paper presents a comprehensive survey of facial feature point detection with the assistance of abundant manually labeled images. Facial feature point detection favors many applications such as face recognition, animation, tracking, hallucination, expression analysis and 3D face modeling. Existing methods can be categorized into the following four groups: constrained local model (CLM)-based, active appearance model (AAM)-based, regression-based, and other methods. CLM-based methods consist of a shape model and a number of local experts, each of which is utilized to detect a facial feature point. AAM-based methods fit a shape model to an image by minimizing texture synthesis errors. Regression-based methods directly learn a mapping function from facial image appearance to facial feature points. Besides the above three major categories of methods, there are also minor categories of methods which we classify into other methods: graphical model-based methods, joint face alignment methods, independent facial feature point detectors, and deep learning-based methods. Though significant progress has been made, facial feature point detection is limited in its success by wild and real-world conditions: variations across poses, expressions, illuminations, and occlusions. A comparative illustration and analysis of representative methods provide us a holistic understanding and deep insight into facial feature point detection, which also motivates us to explore promising future directions. Example learning-based superresolution (SR) algorithms show promise for restoring a high-resolution (HR) image from a single low-resolution (LR) input. The most popular approaches, however, are either time- or space-intensive, which limits their practical applications in many resource-limited settings. In this paper, we propose a novel computationally efficient single image SR method that learns multiple linear mappings (MLM) to directly transform LR feature subspaces into HR subspaces. In particular, we first partition the large nonlinear feature space of LR images into a cluster of linear subspaces. Multiple LR subdictionaries are then learned, followed by inferring the corresponding HR subdictionaries based on the assumption that the LR–HR features share the same representation coefficients. We establish MLM from the input LR features to the desired HR outputs in order to achieve fast yet stable SR recovery. Furthermore, in order to suppress displeasing artifacts generated by the MLM-based method, we apply a fast nonlocal means algorithm to construct a simple yet effective similarity-based regularization term for SR enhancement. Experimental results indicate that our approach is both quantitatively and qualitatively superior to other application-oriented SR methods, while maintaining relatively low time and space complexity.