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Reducing Videoconferencing Fatigue through Facial Emotion Recognition

Jannik Rößler, Jiachen Sun, Peter Gloor
2021 Future Internet  
Emotion was tracked through Zoom face video snapshots using facial emotion recognition that recognized six emotions (happy, sad, fear, anger, neutral, and surprise).  ...  We have studied the influence of emotions of meeting participants on the perceived outcome of video meetings.  ...  Thanks to deep neural networks with convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory (LSTM) models in particular, researchers can extract emotions from facial  ... 
doi:10.3390/fi13050126 fatcat:4al2nvantvhejaxqdosw5elvyi

Recognition of Emotion From Facial Expression for Autism Disorder

2019 International journal of recent technology and engineering  
Use The Camera to capture the live images of autism people  ...  Despite the fact that people immediately perceive facial expressions for all intents and purposes, solid expression recognition by machine is still a challenge.  ...  by using Deep convolutional neural network(DCNN) this input images run continuously and they get the predictions from the train model and in the output screen we will we having the live state of emotion  ... 
doi:10.35940/ijrte.b1095.0782s319 fatcat:6sqqyhgkznbexms6brz6lf454i

Analyzing Real Time Human Emotions using Deep Learning Technique in Machine Learning

MV. Ramana
2020 International Journal for Research in Applied Science and Engineering Technology  
For analyzing real time human emotions, we used Convolutional neural network for recognizing facial emotions from real time videos using TensorFlow as backend.  ...  The system without fail detects the face using HAAR classifier then it crops and resize the image to a specific size and give it to the model for predicting the current human emotion.  ...  In [3] , Authors fought for a system for emotion detection from facial expressions using deep neural networks.  ... 
doi:10.22214/ijraset.2020.5139 fatcat:c4tq7jio3vbrfif6d3yel42kgi

Classifying Emotion Using Convolutional Neural Networks

Jonathan L Moran
2019 UC Merced Undergraduate Research Journal  
In this paper, we will be exploring the concepts of object recognition and deep learning neural networks to ultimately train a classification model to recognize universal human emotion from the FER-2013  ...  Gender, ethnicity, age and emotional state is often perceived immediately by most humans engaging in conversation.  ...  Despite being highly theorized, emotions are relatively easy to perceive from person to person.  ... 
doi:10.5070/m4111041558 fatcat:tzn3lg4lr5g6tdm47yafcqd3su

Emotion Detection using Facial Expressions with Convolution Neural Networks

2019 International journal of recent technology and engineering  
Artificial intelligence systems to perceive human feeling have pulled in much research premium, and potential uses of such frameworks flourish, spreading over areas, for example, client mindful showcasing  ...  An epic research theme to be developed in the Human Computer Interaction field is Emotion Recognition utilizing Facial Expressions.  ...  This article displays another model that is equipped for perceiving emotion detection from facial expressions by utilizing profound Convolution Neural Network (CNN) Algorithm uses MPEG7 color and edge  ... 
doi:10.35940/ijrte.b1452.0982s1119 fatcat:ydns3zyssrgvtfiyhwrparpjxi

Multimodal Emotion Recognition using Deep Learning

Sharmeen M.Saleem Abdullah Abdullah, Siddeeq Y. Ameen Ameen, Mohammed Mohammed sadeeq, Subhi Zeebaree
2021 Journal of Applied Science and Technology Trends  
Multiple techniques can be defined through human feelings, including expressions, facial images, physiological signs, and neuroimaging strategies.  ...  This paper presents a review of emotional recognition of multimodal signals using deep learning and comparing their applications based on current studies.  ...  I COMBINING SIGNALS FROM AUDIO AND TEXT, IMAGE AND TEXT Author Neural network architecture and deep learning technique (algorithms) Accuracy Data set used classificatio n method [ Zexu  ... 
doi:10.38094/jastt20291 fatcat:2ofkuynxebgb5glhsaii5zcq4u

Mood Based Music Playlist Generator Using Convolutional Neural Network

Prof. Jaychand Upadhyay, Sharan Shetty, Vaibhav Murari, Jarvis Trinidade
2022 International Journal for Research in Applied Science and Engineering Technology  
Through the webcam, the emotional state can be deduced from facial expressions. To create a neural network model, the CNN classifier was used.  ...  Keywords: Convolutional neural networks, Facial emotion recognition, FER2013, Music, OpenCV.  ...  predict the emotion through the image.  ... 
doi:10.22214/ijraset.2022.40668 fatcat:d5lcizbsrng6nclufhzsrudow4

Facial Expression Recognition Using Visual Saliency and Deep Learning

Viraj Mavani, Shanmuganathan Raman, Krishna P. Miyapuram
2017 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)  
Further, the image product of the cropped faces and their visual saliency maps were computed using Deep Multi-Layer Network for saliency prediction and were fed to the facial expression recognition CNN  ...  We have developed a convolutional neural network for the purpose of recognizing facial expressions in human beings.  ...  Neural Network [2] .  ... 
doi:10.1109/iccvw.2017.327 dblp:conf/iccvw/MavaniRM17 fatcat:oloo4cilrzerhc2gxrzwt44gvu

Facial Expression Recognition using Visual Saliency and Deep Learning [article]

Viraj Mavani, Shanmuganathan Raman, Krishna P Miyapuram
2017 arXiv   pre-print
Further, the image product of the cropped faces and their visual saliency maps were computed using Deep Multi-Layer Network for saliency prediction and were fed to the facial expression recognition CNN  ...  We have developed a convolutional neural network for the purpose of recognizing facial expressions in human beings.  ...  Neural Network [2] .  ... 
arXiv:1708.08016v1 fatcat:kfpxhbonjfedvgqrntxx7yzz7e

Estimating Attention of Faces Due to its Growing Level of Emotions

Ravi Kant Kumar, Jogendra Garain, Dakshina Ranjan Kisku, Goutam Sanyal
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
The concept of deep convolution neural network (CNN) has been applied for training and classification of different facial expression of emotions.  ...  Moreover, saliency score of every face of the input image have been computed by measuring their 'emotion score' which depends upon the deviation from the 'normal expression' scores.  ...  The convolution neural network receives input images through the input layer.  ... 
doi:10.1109/cvprw.2018.00261 dblp:conf/cvpr/KumarGKS18 fatcat:3bv2dnzyzfdkrnpedjfdoljxe4

Facial Expression Recognition System using Convolutional Neural Networks

2019 International journal of recent technology and engineering  
Convolutional Neural Networks accomplish better precision with huge information.  ...  In this work, we propose a straightforward answer for outward appearance acknowledgment that utilizes a blend of Convolutional Neural Network and explicit picture pre-handling steps.  ...  Block Diagram CONCLUSION In this paper the proposed work uses Convolution Neural Network and Deep Learning technologies to detect the emotion of the face.  ... 
doi:10.35940/ijrte.b1119.0782s419 fatcat:ncl6wznmqvexvazp77uu52przy

Face Expression Recognition using Convolution Neural Network (CNN) Models

Nahla Nour, Mohammed Elhebir, Serestina Viriri
2020 International Journal of Grid Computing & Applications  
This paper proposes the design of a Facial Expression Recognition (FER) system based on deep convolutional neural network by using three model.  ...  Convolution Neural Network Convolutional neural networks are presently among the best flamboyant algorithms for deep learning with image data.  ...  Problems remain even when deep learning is applied to FER despite its feature learning ability. Firstly, many training data are required by deep neural networks to be free from over fitting.  ... 
doi:10.5121/ijgca.2020.11401 fatcat:edsopsoksnh7nmtikkzobqicoi

Predicting and visualizing psychological attributions with a deep neural network [article]

Edward Grant, Stephan Sahm, Mariam Zabihi, Marcel van Gerven
2016 arXiv   pre-print
We demonstrate a Convolutional Neural Network (CNN) model that is able to perform the same task without the need for landmark features, thereby greatly increasing efficiency.  ...  The most successful of these approaches require face images expertly annotated with key facial landmarks.  ...  DISCUSSION This work shows that deep neural networks can be used to accurately predict rated personality traits from face images, even surpassing human-level performance in some cases.  ... 
arXiv:1512.01289v2 fatcat:a65dyd7sdjfkhmhy6yvy7efvzi

Prediction of Perceived Utility of Consumer Online Reviews Based on LSTM Neural Network

Hu Wang, Tianbao Liang, Yanxia Cheng, Sang-Bing Tsai
2021 Mobile Information Systems  
The research shows that using deep neural network to predict the perceived utility of consumer comments can reduce the intervention of artificial features and labor costs and help predict the perceived  ...  It is a challenging task to analyze the emotion of consumers or recognize the perceived value of consumers from various texts of online trading platforms.  ...  Proposed Method LSTM Neural Network Concept of LSTM. A recurrent neural network is a kind of artificial neural network. It has memory properties, including long-term memory and short-term memory.  ... 
doi:10.1155/2021/5482662 fatcat:6a2upz4tn5dyhlfzbntvxcjpgq

Building Emotional Machines: Recognizing Image Emotions through Deep Neural Networks [article]

Hye-Rin Kim, Yeong-Seok Kim, Seon Joo Kim, In-Kwon Lee
2017 arXiv   pre-print
By combining the different levels of features, we build an emotion based feed forward deep neural network which produces the emotion values of a given image.  ...  An image is a very effective tool for conveying emotions. Many researchers have investigated in computing the image emotions by using various features extracted from images.  ...  Despite the rise of deep learning studies, relatively few studies have attempted to address the emotion prediction of images using the deep network. With the data set from Borth et al.  ... 
arXiv:1705.07543v2 fatcat:yw445jxscjbadnyiqr2bj3biru
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