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Face hallucination is a super‐resolution technique specially designed to reconstruct high‐resolution faces from low‐resolution faces. Most state‐of‐the‐art algorithms leverage position‐patch prior knowledge of human faces to better super‐resolve face images. However, most of them assume the training face dataset is sufficiently large, well cropped or aligned. This paper, proposes a novel example‐based face hallucination method, based on cluster consistent dictionary learning with the assumption that human faces have similar facial structures. In this method, the paired face image patches are firstly labelled as face areas including eyes, nose, mouth and other parts, as well as non‐face areas without requiring the training face images cropped and aligned. Then, the training patches are clustered according their labels and textures. The cluster consistent dictionary is learned to represent the low‐resolution patches and the high‐resolution patches. Finally, the high‐resolution patches of the input low‐resolution face image can be efficiently generated by using the adjusted anchored neighbourhood regression. As utilizing the labelled facial parts prior knowledge, the proposed method represents more details in the reconstruction. Experimental results demonstrate that the authors' algorithm outperforms many state‐of‐the‐art techniques for face hallucination under different datasets. [ABSTRACT FROM AUTHOR]

Highlights • This paper provides a systematic review of current histogram-based local feature descriptors, which have been applied for facial-expression recognition. • The weaknesses and strengths of the existing descriptors are discussed and analysed. • A comprehensive evaluation of the performances of 27 local descriptors is presented. • The best overall performances are obtained by LPQ and LGBPHS. This paper aims to present histogram-based local descriptors applied to Facial Expression Recognition (FER) from static images and provide a systematic review and analysis of them. First, we describe the main steps in encoding binary patterns in a local patch, which are required in every histogram-based local descriptor. Then, we list the existing local descriptors, while analysing their strengths and weaknesses. Finally, we present the experimental results of all these descriptors on commonly used facial expression databases, with varying resolution, noise, occlusion, and number of sub-regions, as well as comparing them with the results obtained by the state-of-the-art deep learning methods. This paper aims to bring together different studies of the visual features for FER by evaluating their performances under the same experimental setup, and critically reviewing various classifiers making use of the local descriptors. [ABSTRACT FROM AUTHOR]

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]

Abstract: In this paper, an efficient method for human facial expression recognition is presented. We first propose a representation model for facial expressions, namely the spatially maximum occurrence model (SMOM), which is based on the statistical characteristics of training facial images and has a powerful representation capability. Then the elastic shape–texture matching (ESTM) algorithm is used to measure the similarity between images based on the shape and texture information. By combining SMOM and ESTM, the algorithm, namely SMOM–ESTM, can achieve a higher recognition performance level. The recognition rates of the SMOM–ESTM algorithm based on the AR database and the Yale database are 94.5% and 94.7%, respectively. [Copyright &y& Elsevier]

Abstract: In this paper, we propose a new face recognition algorithm based on a single frontal-view image for each face subject, which considers the effect of the face manifold structure. To compare two near-frontal face images, each face is considered a combination of a sequence of local image blocks. Each of the image blocks of one image can be reconstructed according to the corresponding local image block of the other face image. Then an elastic local reconstruction (ELR) method is proposed to measure the similarities between the image block pairs in order to measure the difference between the two face images. Our algorithm not only benefits from the face manifold structure, in terms of being robust to various image variations, but also is computationally simple because there is no need to build the face manifold. We evaluate the performance of our proposed face recognition algorithm with the use of different databases, which are produced under various conditions, e.g. lightings, expressions, perspectives, with/without glasses and occlusions. Consistent and promising experimental results were obtained, which show that our algorithm can greatly improve the recognition rates under all the different conditions. [Copyright &y& Elsevier]

Abstract: The active shape model (ASM) has been used successfully to extract the facial features of a face image under frontal view. However, its performance degrades when the face concerned is under perspective variations. In this paper, a modified shape model is proposed which can adapt to face images under different orientations. To make the model represent a face more flexibly, the representations of the important facial features, i.e. the eyes, nose and mouth, and the face contour are separated. An energy function is defined that links up these two representations of a human face. In order to represent a face image under different poses, three models are employed to represent the important facial features: the left-viewed, right-viewed, and frontal-viewed models. The genetic algorithm (GA) is applied to search for the best representation of face images. Experimental results demonstrate that our proposed method can achieve a better performance in representing face images under different perspective variations and facial expressions than the conventional ASM can. [Copyright &y& Elsevier]