scholarly journals Facial Emotion Detection through Deep Covolutional Neural Networks

2018 ◽  
Vol 15 (3) ◽  
pp. 113
Author(s):  
Aymun Saif Dar ◽  
Sheraz Naseer ◽  
Aihtshan Ali ◽  
Ishmal Sauf ◽  
Muhammad Ahsan
Keyword(s):  
Neural Networks ◽  
Facial Emotion ◽  
Emotion Detection

Usage of Convolutional Neural Networks in Real-Time Facial Emotion Detection

2021 ◽  
pp. 259-273
Author(s):  
Ch. Sanjeev Kumar Dash ◽  
Ajit Kumar Behera ◽  
Sarat Chandra Nayak ◽  
Satchidananda Dehuri
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Facial Emotion ◽  
Emotion Detection

scholarly journals IoMT Based Facial Emotion Recognition System Using Deep Convolution Neural Networks

Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1289
Author(s):  
Navjot Rathour ◽  
Sultan S. Alshamrani ◽  
Rajesh Singh ◽  
Anita Gehlot ◽  
Mamoon Rashid ◽  
...  
Keyword(s):  
Neural Networks ◽  
Emotion Recognition ◽  
Real Time ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Human Beings ◽  
Emotion Detection ◽  
Resource Constrained

Facial emotion recognition (FER) is the procedure of identifying human emotions from facial expressions. It is often difficult to identify the stress and anxiety levels of an individual through the visuals captured from computer vision. However, the technology enhancements on the Internet of Medical Things (IoMT) have yielded impressive results from gathering various forms of emotional and physical health-related data. The novel deep learning (DL) algorithms are allowing to perform application in a resource-constrained edge environment, encouraging data from IoMT devices to be processed locally at the edge. This article presents an IoMT based facial emotion detection and recognition system that has been implemented in real-time by utilizing a small, powerful, and resource-constrained device known as Raspberry-Pi with the assistance of deep convolution neural networks. For this purpose, we have conducted one empirical study on the facial emotions of human beings along with the emotional state of human beings using physiological sensors. It then proposes a model for the detection of emotions in real-time on a resource-constrained device, i.e., Raspberry-Pi, along with a co-processor, i.e., Intel Movidius NCS2. The facial emotion detection test accuracy ranged from 56% to 73% using various models, and the accuracy has become 73% performed very well with the FER 2013 dataset in comparison to the state of art results mentioned as 64% maximum. A t-test is performed for extracting the significant difference in systolic, diastolic blood pressure, and the heart rate of an individual watching three different subjects (angry, happy, and neutral).

Facial Emotion Detection to Assess Learner's State of Mind in an Online Learning System

2020 ◽  
Author(s):  
Moutan Mukhopadhyay ◽  
Saurabh Pal ◽  
Anand Nayyar ◽  
Pijush Kanti Dutta Pramanik ◽  
Niloy Dasgupta ◽  
...  
Keyword(s):  
Online Learning ◽  
Learning System ◽  
Facial Emotion ◽  
Emotion Detection ◽  
State Of Mind

scholarly journals Emotion Detection of Human Face

2019 ◽  
Vol 9 (1) ◽  
pp. 5521-5524 ◽  
Keyword(s):  
Neural Network ◽  
Facial Expressions ◽  
Language Translation ◽  
Online Marketing ◽  
Facial Emotion ◽  
Good Resolution ◽  
Human Beings ◽  
Emotion Detection ◽  
Unsupervised Neural Network ◽  
User Device

Facial emotion analysis is the basic idea to train the system to understand the different facial expressions of human beings. The Facial expressions are recorded by the use of camera which is attached to user device. Additionally this project will be helpful for the online marketing of the products as it can detect the facial expressions and sentiment of the person. It is the study of people sentiment, opinions and emotions. Sentiment analysis is the method by which information is taken from the facial expressions of people in regard to different situations. The main aim is to read the facial expressions of the human beings using a good resolution camera so that the machine can identify the human sentiments. Convolutional neural network is used as an existing system which is unsupervised neural network to replace that with a supervised mechanism which is called supervised neural network. It can be used in gaming sector, unlock smart phones, automated facial language translation etc.

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