Emotion recognition plays a significant role in cognitive psychology research. However, measuring emotions is a challenging task. Thus, several approaches have been designed for facial expression recognition (FER). Although, the challenges increases further as the data transits from laboratory-controlled environment to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-the-wild circumstances. Nowadays, applications are overwhelmed by a profusion of deep learning (DL) techniques in the real-world problems. DL networks have steadily led to a better understanding of low-dimensional discriminative features from high-dimensional complex face patterns for automatic FER. The modern FER systems based on deep neural networks mainly suffer from two problems: overfitting due to the inadequate availability of training data and complications unassociated with the expressions, such as occlusion, posture, illumination, and identity bias. This study aims to provide a comprehensive survey of the significant DL-based methods that have made a notable contribution to the field of FER. Different components of the methods, such as pre-processing, feature extraction, and classification of facial expressions, are described systematically. Moreover, the discussed approaches are analyzed to compare their performance along with their advantages and limitations. Further, different databases relevant to FER are also explored in this study. Essentially, the main aim of this survey is twofold. The former is to discuss the current scenario of FER approaches and the latter is to present some thoughts on the future directions of facial emotion recognition by machines: what are the obstacles and prospects for FER researchers?.

Understanding Deep Learning Techniques for Recognition of Human Emotions Using Facial Expressions: A Comprehensive Survey

Emotion recognition plays a significant role in cognitive psychology research. However, measuring emotions is a challenging task. Thus, several approaches have been designed for facial expression recognition (FER). Although, the challenges increase further as the data transit from the laboratory-controlled environment to in-the-wild circumstances, nowadays, applications are overwhelmed by a profusion of deep learning (DL) techniques in real-world problems. DL networks have steadily led to a better understanding of low-dimensional discriminative features from high-dimensional complex face patterns for automatic FER. The modern FER systems based on deep neural networks mainly suffer from two problems: overfitting due to the inadequate availability of training data and complications unassociated with the expressions, such as occlusion, posture, illumination, and identity bias. This study aims to provide a comprehensive survey of the significant DL-based methods that have made a notable contribution to the field of FER. Different components of the methods, such as preprocessing, feature extraction, and classification of facial expressions, are described systematically. Moreover, the discussed approaches are analyzed to compare their performance along with their advantages and limitations. Furthermore, different databases relevant to FER are also explored in this study. Essentially, the main aim of this survey is twofold. The former is to discuss the current scenario of FER approaches and the latter is to present some thoughts on the future directions of facial emotion recognition by machines: what are the obstacles and prospects for FER researchers?

In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features. However, such features have low discriminating power. We propose a deep-learning-based stress-recognition method using facial images to address these challenges. Given that deep-learning methods require extensive data, we constructed a large-capacity image database for stress recognition. Furthermore, we used temporal attention, which assigns a high weight to frames that are highly related to stress, as well as spatial attention, which assigns a high weight to regions that are highly related to stress. By adding a network that inputs the facial landmark information closely related to stress, we supplemented the network that receives only facial images as the input. Experimental results on our newly constructed database indicated that the proposed method outperforms contemporary deep-learning-based recognition methods.

Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information.

Ground-based cloud images can provide information on weather and cloud conditions, which play an important role in cloud cover monitoring and photovoltaic power generation forecasting. However, the cloud motion prediction of ground-based cloud images still lacks advanced and complete methods, and traditional technologies based on image processing and motion vector calculation are difficult to predict cloud morphological changes. In this paper, we propose a cloud motion prediction method based on Cascade Causal Long Short-Term Memory (CCLSTM) and Super-Resolution Network (SR-Net). Firstly, CCLSTM is used to estimate the shape and speed of cloud motion. Secondly, the Super-Resolution Network is built based on perceptual losses to reconstruct the result of CCLSTM and, finally, make it clearer. We tested our method on Atmospheric Radiation Measurement (ARM) Climate Research Facility TSI (total sky imager) images. The experiments showed that the method is able to predict the sky cloud changes in the next few steps.

A Method of Ground-Based Cloud Motion Predict: CCLSTM + SR-Net

Abstract Learning statistical models successfully is both an essential and a challenging task for various pattern recognition and knowledge discovery applications. In particular, generative models such as finite and infinite mixture models have demonstrated to be efficient in terms of overall performance. In this paper, a robust framework based on an expectation propagation (EP) inference is developed to learn inverted Beta-Liouville (IBL) mixture models which is proper choice for positive data classification. Within the proposed EP learning method, the full posterior distribution is estimated accurately, the model complexity and all related parameters are evaluated simultaneously in a single optimization scheme. Extensive experiments using challenging real-world applications including recognition of facial expression, automatic human action categorization, and hand gesture recognition show the merit of our approach in terms of achieving better results than comparable techniques.

Unsupervised Learning Using Expectation Propagation Inference of Inverted Beta-Liouville Mixture Models for Pattern Recognition Applications

This paper addresses the problem of Facial Expression Recognition (FER), focusing on unobvious facial movements. Traditional methods often cause overfitting problems or incomplete information due to insufficient data and manual selection of features. Instead, our proposed network, which is called the Multi-features Cooperative Deep Convolutional Network (MC-DCN), maintains focus on the overall feature of the face and the trend of key parts. The processing of video data is the first stage. The method of ensemble of regression trees (ERT) is used to obtain the overall contour of the face. Then, the attention model is used to pick up the parts of face that are more susceptible to expressions. Under the combined effect of these two methods, the image which can be called a local feature map is obtained. After that, the video data are sent to MC-DCN, containing parallel sub-networks. While the overall spatiotemporal characteristics of facial expressions are obtained through the sequence of images, the selection of keys parts can better learn the changes in facial expressions brought about by subtle facial movements. By combining local features and global features, the proposed method can acquire more information, leading to better performance. The experimental results show that MC-DCN can achieve recognition rates of 95%, 78.6% and 78.3% on the three datasets SAVEE, MMI, and edited GEMEP, respectively.

Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

In order to solve the problem of identifying severe convection weather, this paper proposes a model of identifying severe convection weather based on rough set theory and artificial fish swarm algorithm. First, based on the radar reflectivity images, the echo attributes of the severe convection area are analyzed and extracted. Second, this paper proposes a discretization method based on rough set theory. This method discretizes the meteorological data and establishes a decision table. Third, the improved artificial fish swarm algorithm is used to reduce attributes and obtain the minimum attribute set. Finally, the classification and regression tree (CART) algorithm and the cost-complexity pruning (CCP) principle are used to establish an objective model for identifying severe convection weather. Experimental results show that the model in this paper can recognize severe convection weather under the condition of a few attributes and the identification model has high accuracy.

Severe Convection Weather Identification Model Based on Rough Set Theory and Artificial Fish Swarm Algorithm

In the field of meteorology, radiosonde data and observation data are critical for analyzing regional meteorological characteristics Because of the high false alarm rate, severe convection forecasting is still challenging In addition, the existing methods are difficult to use to capture the interaction of meteorological factors at the same time In this research, a cascade of extreme gradient boosting (XGBoost) for feature transformation and a factorization machine (FM) for second-order feature interaction to capture the nonlinear interaction—XGB+FM—is proposed An attention-based bidirectional long short-term memory (Att-Bi-LSTM) network is proposed to impute the missing data of meteorological observation stations The problem of class imbalance is resolved by the support vector machines–synthetic minority oversampling technique (SVM-SMOTE), in which two oversampling strategies based on the support vector discrimination mechanism are proposed It is proven that the method is effective, and the threat score (TS) is 727~1428% higher than other methods Moreover, we propose the meteorological factor selection method based on XGB+FM and improve the forecast accuracy, which is one of our contributions, as well as the forecast system

https://www.mdpi.com/2079-9292/10/3/321/pdf

XGB+FM for Severe Convection Forecast and Factor Selection

With the development of deep learning, the perception of facial image has wide prospects of application.In a crowded environment, there is a problem that faces cannot be detected due to mutual occlusion of faces, and the low resolution causes the face to be blurred. In order to detect the missing faces which were occluded in the dense environments, a kind of model, which based on multi-task cascaded convolutional network, was put forth. We improve the detection scheme by using Soft non-maximum suppression algorithm.Aiming at the problem of faces with proportion of inconsistent caused by the inconsistency of the distance between the face and the camera device, this paper extracts facial features based on the deep res-net. For high and low resolution facial images, a dual-branch extraction feature network is used to make images of different resolutions are clustered in a public feature space. At the same time, the central loss function, parameterized rectification linear unit and batch normalization are used to improve the network training process, which improves the network's feature classification performance and the convergence speed in the training process. WIDER FACE database is used to verify the face detection method. LFW database and YALE face database are used to verify face recognition method. Experiments show that the network proposed in this paper has good robustness against occlusion and low resolution in face detection, and effectively expands the distance between classes in face recognition and improves the recognition accuracy.

Face Detection And Recognition In Complex Environments

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Papers

Facial Expression Recognition Based on Multi-Features Cooperative Deep Convolutional Network

Severe Convection Weather Identification Model Based on Rough Set Theory and Artificial Fish Swarm Algorithm

XGB+FM for Severe Convection Forecast and Factor Selection

Face Detection And Recognition In Complex Environments