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At present, poor background suppression is one major problem for visual saliency detection. Although many mainstream saliency detection models can effectively locate salient objects, objects in complicated backgrounds in some natural images are often mistaken for salient objects. Therefore, this paper proposed to set up a center prior-based encoder-decoder network to improve background suppression results. A traditional center prior-based method and U-Net model were combined efficiently. First, multi-scale group convolution was used to replace general convolution, which can highlight the semantic information characteristics, and high-level characteristics at the bottom of U-Net were integrated and optimized on the basis of the consideration of center prior. Then, refinements were delivered throughout the whole network by upgrading the network structure, so as to ensure the optimized features can be made full use of. Since the changes to the U-Net architecture somewhat affected the stability of the network. Therefore, branch network modules were adopted and adaptive parameters defined to coordinate the relationships between branch networks to keep the network structure well balanced. The method has been tested with four widely used databases and proven effective by comparing its results with those of another seven popular methods. [ABSTRACT FROM AUTHOR]

While the relationship between facial expressions and emotion has been a productive area of inquiry, research is only recently exploring whether a link exists between facial expressions and cognitive processes. Using findings from psychology and neuroscience to guide predictions of affectation during a cognitive task, this article aimed to study facial dynamics as a mean to understand comprehension. We present a new multimodal facial expression database, named Facial Expressions of Comprehension (FEC), consisting of the videos recorded during a computer-mediated task in which each trial consisted of reading, answering, and feedback to general knowledge true and false statements. To identify the level of engagement with the corresponding stimuli, we present a new methodology using animation units (AnUs) from the Kinect v2 device to explore the changes in facial configuration caused by an event: Event-Related Intensities (ERIs). To identify dynamic facial configurations, we used ERIs in statistical analyses with generalized additive models. To identify differential facial dynamics linked to knowing vs. guessing and true vs. false responses, we employed an SVM classifier with facial appearance information extracted using LPQ-TOP. Results of ERIs in sentence comprehension show that facial dynamics are promising to help understand affective and cognitive states of the mind. [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]

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]

In this paper, we propose a novel and efficient algorithm to reconstruct the 3-D structure of a human face from one or a number of its 2-D images with different poses. In our proposed algorithm, the rotation and translation process from a frontal-view face image to a nonfrontal-view face image is at first formulated as a constrained independent component analysis (cICA) model. Then, the overcomplete ICA problem is converted into a normal ICA problem by incorporating a prior from the CANDIDE 3-D face model. Furthermore, the CANDIDE model is employed to construct a reference signal that is used in both the initialization and the objective function of the cICA model. Moreover, a model-integration method is proposed to improve the depth-estimation accuracy when multiple nonfrontal-view face images are available. An important advantage of the proposed algorithm is that no frontal-view face image is required for the estimation of the corresponding 3-D face structure. Experimental results on a real 3-D face image database demonstrate the feasibility and efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]

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: In this paper, a novel, elastic, shape-texture matching method, namely ESTM, for human face recognition is proposed. In our approach, both the shape and the texture information are used to compare two faces without establishing any precise pixel-wise correspondence. The edge map is used to represent the shape of an image, while the texture information is characterized by both the Gabor representations and the gradient direction of each pixel. Combining these features, a shape-texture Hausdorff distance is devised to compute the similarity of two face images. The elastic matching is robust to small, local distortions of the feature points such as those caused by facial expression variations. In addition, the use of the edge map, Gabor representations and the direction of the image gradient can all alleviate the effect of illumination to a certain extent. With different databases, experimental results show that our algorithm can always achieve a better performance than other face recognition algorithms under different conditions, except when an image is under poor and uneven illumination. Experiments based on the Yale database, AR database, ORL database and YaleB database show that our proposed method can achieve recognition rates of 88.7%, 97.7%, 78.3% and 89.5%, respectively. [Copyright &y& Elsevier]