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?

Significant progress has been made with artistic robots. However, existing robots fail to produce high-quality portraits in a short time. In this work, we present a drawing robot, which can automatically transfer a facial picture to a vivid portrait, and then draw it on paper within two minutes averagely. At the heart of our system is a novel portrait synthesis algorithm based on deep learning. Innovatively, we employ a self-consistency loss, which makes the algorithm capable of generating continuous and smooth brush-strokes. Besides, we propose a componential sparsity constraint to reduce the number of brush-strokes over insignificant areas. We also implement a local sketch synthesis algorithm, and several pre- and post-processing techniques to deal with the background and details. The portrait produced by our algorithm successfully captures individual characteristics by using a sparse set of continuous brush-strokes. Finally, the portrait is converted to a sequence of trajectories and reproduced by a 3-degree-of-freedom robotic arm. The whole portrait drawing robotic system is named AiSketcher. Extensive experiments show that AiSketcher can produce considerably high-quality sketches for a wide range of pictures, including faces in-the-wild and universal images of arbitrary content. To our best knowledge, AiSketcher is the first portrait drawing robot that uses neural style transfer techniques. AiSketcher has attended a quite number of exhibitions and shown remarkable performance under diverse circumstances.

Making Robots Draw A Vivid Portrait In Two Minutes

Various industrial fields use gas-liquid separation, but due to the variety of gas-liquid separation technologies, no comprehensive review has been conducted. This paper systematically reviews the separation mechanism, application ranges, research status, and development prospects of various separation techniques. The challenges associated with each gas-liquid separation technology are presented. This review indicates that future research should focus on the droplet-droplet and droplet-gas interaction mechanisms and the formation and breakup mechanisms of liquid films. Future experimental efforts should focus on investigating gas-liquid two-phase flow fields including droplet size distribution, phase distribution, and velocity distribution. A promising development direction is to combine several gas-liquid separation technologies to expand their range of applications. An increased knowledge of the relationship between the flow field and separation performance would guide the standardized design of the gas-liquid separators. 

A review of gas-liquid separation technologies: Separation mechanism, application scope, research status, and development prospects

This article describes the algorithm of points extraction from the picture processed by a canny edge detector that will be turned in the curves using the cubic spline interpolation. Here will be presented several examples, including code. Furthermore, this paper will describe steps of creating the system, which will depict on a sheet of paper a picture taken from a web-camera. The system enables a webcam from which will be taken snapshot, the convenient program with the graphical user interface, including algorithms, whose parameters can be changed, TCP server, webcam control, observation of the drawing process and logging. The main drawing component of the system is KUKA KR3 R540. KR3 R540 is robotic system with a manipulator that will take commands to draw a result image.

Face Drawing by KUKA 6 Axis Robot Manipulator

Purification technology of oil mist in industrial buildings: A review

Facial expression is important indicator to human health status. This paper is devoted to improving the safety monitor and healthcare for the old through facial expression recognition (FER). A novel convolutional neural network (CNN) architecture which is able to accelerate training process was proposed to deal with FER problems. In order to improve the FER performance in real life, a new dataset was collected to ease the data imbalance of FER2013 dataset. Further more, the method of moving average is ultilized to make up for the drawbacks of still image-based approaches, which is efficient for smoothing the real-time FER results. To monitor the safety and health status of the old, a digital healthcare (DHc) framework was proposed for better healthcare. As a result, the proposed model achieved compariable performance to the state-of-the-art methods both on FER2013 and NVIE datasets. The robot, equipped with the DHc framework, can recognize facial expression in real-life with high performance, and achieved substantial improvement for safety monitor and healthcare of the old.

Real-time Facial Expression Recognition on Robot for Healthcare

This paper presents a head detection and extraction method that can be used in robotic portrait drawing. First, using the state-of-the-art, real-time object detection system-YOLO(You Only Look Once), we train the model to automatically detect human heads directly from the image. Then we utilize the holistically-nested edge detection (HED) algorithm to extract head edges by performing image-to-image prediction. Finally, the content image of the head can be synthesized into a head edge map with a style synthesis algorithm. The synthesized image can be sent to the robot for drawing. Our method was verified and evaluated on the drawing robot we developed.

Deep learning-based human head detection and extraction for robotic portrait drawing

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Purification technology of oil mist in industrial buildings: A review

Real-time Facial Expression Recognition on Robot for Healthcare

Deep learning-based human head detection and extraction for robotic portrait drawing