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A two-step face-hallucination framework is proposed to reconstruct a high-resolution (HR) version of a face from an input low-resolution (LR) face, based on learning from LR-HR example face pairs using orthogonal canonical correlation analysis (orthogonal CCA) and linear mapping. In the proposed algorithm, face images are first represented using principal component analysis (PCA). Canonical correlation analysis (CCA) with the orthogonality property is then employed, to maximize the correlation between the PCA coefficients of the LR and the HR face pairs to improve the hallucination performance. The original CCA does not own the orthogonality property, which is crucial for information reconstruction. We propose using orthogonal CCA, which is proven by experiments to achieve a better performance in terms of global face reconstruction. In addition, in the residual-compensation process, a linear-mapping method is proposed to include both the interand intrainformation about manifolds of different resolutions. Compared with other state-of-the-art approaches, the proposed framework can achieve a comparable, or even better, performance in terms of global face reconstruction and the visual quality of face hallucination. Experiments on images with various parameter settings and blurring distortions show that the proposed approach is robust and has great potential for real-world applications. ©2016SPIE and IS&T [DOI: 10.1117/1.JEI.25.3.033005] [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an efficient mapping model based on singular value decomposition (SVD) is proposed for face hallucination. We can observe and prove that the main singular values of an image at one resolution have approximately linear relationships with their counterparts at other resolutions. This makes the estimation of the singular values of the corresponding high-resolution (HR) face images from a low-resolution (LR) face image more reliable. From the signal-processing point of view, this can effectively preserve and reconstruct the dominant information in the HR face images. Interpolating the other two matrices obtained from the SVD of the LR image does not change either the primary facial structure or the pattern of the face image. The corresponding two matrices for the HR face images can be constructed in a “coarse-to-fine” manner using global reconstruction. Our proposed method retains the holistic structure of face images, while the learned mapping matrices, which are represented as embedding coefficients of the individual mapping matrices learned from LR-HR training pairs, can be seen as holistic constraints in the reconstruction of HR images. Compared to state-of-the-art algorithms, experiments show that our proposed face-hallucination scheme is effective in terms of producing plausible HR images with both a holistic structure and high-frequency details. [Copyright &y& Elsevier]

Abstract: In this paper, we present a kernel-based eigentransformation framework to hallucinate the high-resolution (HR) facial image of a low-resolution (LR) input. The eigentransformation method is a linear subspace approach, which represents an image as a linear combination of training samples. Consequently, those novel facial appearances not included in the training samples cannot be super-resolved properly. To solve this problem, we devise a kernel-based extension of the eigentransformation method, which takes higher-order statistics of the image data into account. To generate HR face images with higher fidelity, the HR face image reconstructed using this kernel-based eigentransformation method is treated as an initial estimation of the target HR face. The corresponding high-frequency components of this estimation are extracted to form a prior in the maximum a posteriori (MAP) formulation of the SR problem so as to derive the final reconstruction result. We have evaluated our proposed method using different kernels and configurations, and have compared these performances with some current SR algorithms. Experimental results show that our kernel-based framework, along with a proper kernel, can produce good HR facial images in terms of both visual quality and reconstruction errors. [ABSTRACT FROM AUTHOR]

We have developed a new face hallucination framework termed from local pixel structure to global image super-resolution (LPS-GIS). Based on the assumption that two similar face images should have similar local pixel structures, the new framework first uses the input low-resolution (LR) face image to search a face database for similar example high-resolution (HR) faces in order to learn the local pixel structures for the target HR face. It then uses the input LR face and the learned pixel structures as priors to estimate the target HR face. We present a three-step implementation procedure for the framework. Step 1 searches the database for K example faces that are the most similar to the input, and then warps the K example images to the input using optical flow. Step 2 uses the warped HR version of the K example faces to learn the local pixel structures for the target HR face. An effective method for learning local pixel structures from an individual face, and an adaptive procedure for fusing the local pixel structures of different example faces to reduce the influence of warping errors, have been developed. Step 3 estimates the target HR face by solving a constrained optimization problem by means of an iterative procedure. Experimental results show that our new method can provide good performances for face hallucination, both in terms of reconstruction error and visual quality; and that it is competitive with existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating high-resolution (HR) facial images from a single low-resolution (LR) input. We assume that the input LR and estimated HR images are under the same view-point and illumination condition, i.e. the setting of image super-resolution. At the core of our techniques is that the facial images can be decomposed as a texture vector, characterized in terms of the appearance, and a shape vector, characterized in terms of the geometry variations. This enables a two-stage successive estimation framework that is geometry aware and obviates the needs in sophisticated optimizations. In particular, the proposed technique first solves for appearance of the HR faces form the correspondence derived between an interpolated LR face and its corresponding HR face. Given the texture of the HR faces, we incorporate optical flow to solve the local structure at sub-pixel level for the HR faces; here, we use additional geometry inspired priors to further regularize the solution. Experimental results show that our method outperforms other state-of-the-art methods in terms of retaining the facial-feature shape and the estimation of novel features. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]