scholarly journals The Extensive Usage of the Facial Image Threshing Machine for Facial Emotion Recognition Performance

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2026
Author(s):  
Jung Hwan Kim ◽  
Alwin Poulose ◽  
Dong Seog Han
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
System Performance ◽  
Autonomous Vehicles ◽  
Confusion Matrix ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Facial Image ◽  
Facial Images

Facial emotion recognition (FER) systems play a significant role in identifying driver emotions. Accurate facial emotion recognition of drivers in autonomous vehicles reduces road rage. However, training even the advanced FER model without proper datasets causes poor performance in real-time testing. FER system performance is heavily affected by the quality of datasets than the quality of the algorithms. To improve FER system performance for autonomous vehicles, we propose a facial image threshing (FIT) machine that uses advanced features of pre-trained facial recognition and training from the Xception algorithm. The FIT machine involved removing irrelevant facial images, collecting facial images, correcting misplacing face data, and merging original datasets on a massive scale, in addition to the data-augmentation technique. The final FER results of the proposed method improved the validation accuracy by 16.95% over the conventional approach with the FER 2013 dataset. The confusion matrix evaluation based on the unseen private dataset shows a 5% improvement over the original approach with the FER 2013 dataset to confirm the real-time testing.

scholarly journals Real-time facial emotion recognition using lightweight convolution neural network

2021 ◽  
Vol 1827 (1) ◽  
pp. 012130
Author(s):  
Qi Li ◽  
Yun Qing Liu ◽  
Yue Qi Peng ◽  
Cong Liu ◽  
Jun Shi ◽  
...  
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Real Time ◽  
Convolution Neural Network ◽  
Facial Emotion Recognition ◽  
Facial Emotion

scholarly journals Real-Time Facial Emotion Recognition Framework for Employees of Organizations Using Raspberry-Pi

2021 ◽  
Vol 11 (22) ◽  
pp. 10540
Author(s):  
Navjot Rathour ◽  
Zeba Khanam ◽  
Anita Gehlot ◽  
Rajesh Singh ◽  
Mamoon Rashid ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Real Life ◽  
Recognition System ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Raspberry Pi ◽  
Input Device ◽  
Facial Emotions ◽  
Deep Network

There is a significant interest in facial emotion recognition in the fields of human–computer interaction and social sciences. With the advancements in artificial intelligence (AI), the field of human behavioral prediction and analysis, especially human emotion, has evolved significantly. The most standard methods of emotion recognition are currently being used in models deployed in remote servers. We believe the reduction in the distance between the input device and the server model can lead us to better efficiency and effectiveness in real life applications. For the same purpose, computational methodologies such as edge computing can be beneficial. It can also encourage time-critical applications that can be implemented in sensitive fields. In this study, we propose a Raspberry-Pi based standalone edge device that can detect real-time facial emotions. Although this edge device can be used in variety of applications where human facial emotions play an important role, this article is mainly crafted using a dataset of employees working in organizations. A Raspberry-Pi-based standalone edge device has been implemented using the Mini-Xception Deep Network because of its computational efficiency in a shorter time compared to other networks. This device has achieved 100% accuracy for detecting faces in real time with 68% accuracy, i.e., higher than the accuracy mentioned in the state-of-the-art with the FER 2013 dataset. Future work will implement a deep network on Raspberry-Pi with an Intel Movidious neural compute stick to reduce the processing time and achieve quick real time implementation of the facial emotion recognition system.

scholarly journals A Study on Alexithymia, Quality of Life, and Facial Emotion Recognition Abilities in Somatoform Disorders

2018 ◽  
Vol 52 (3) ◽  
pp. 110-116
Author(s):  
Vinuprasad Venugopalan ◽  
Manas Elkal ◽  
Rishikesh V Behere ◽  
Samir K Praharaj ◽  
Haridas Kanaradi
Keyword(s):  
Quality Of Life ◽  
Emotion Recognition ◽  
Somatoform Disorders ◽  
Facial Emotion Recognition ◽  
Facial Emotion

scholarly journals Deep Robot-Human Interaction with Facial Emotion Recognition Using Gated Recurrent Units & Robotic Process Automation

2020 ◽  
Author(s):  
Suchitra Saxena ◽  
Shikha Tripathi ◽  
Sudarshan Tsb
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Research Work ◽  
Human Interaction ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Process Automation ◽  
Training Time ◽  
Robotic Process Automation ◽  
Gated Recurrent Units

This research work proposes a Facial Emotion Recognition (FER) system using deep learning algorithm Gated Recurrent Units (GRUs) and Robotic Process Automation (RPA) for real time robotic applications. GRUs have been used in the proposed architecture to reduce training time and to capture temporal information. Most work reported in literature uses Convolution Neural Networks (CNN), Hybrid architecture of CNN with Long Short Term Memory (LSTM) and GRUs. In this work, GRUs are used for feature extraction from raw images and dense layers are used for classification. The performance of CNN, GRUs and LSTM are compared in the context of facial emotion recognition. The proposed FER system is implemented on Raspberry pi3 B+ and on Robotic Process Automation (RPA) using UiPath RPA tool for robot human interaction achieving 94.66% average accuracy in real time.

scholarly journals Real Time Facial Emotion Recognition based on Image Processing and Machine Learning

2016 ◽  
Vol 139 (11) ◽  
pp. 16-19 ◽  
Author(s):  
Rituparna Halder ◽  
Sushmit Sengupta ◽  
Arnab Pal ◽  
Sudipta Ghosh ◽  
Debashish Kundu
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Emotion Recognition ◽  
Real Time ◽  
Facial Emotion Recognition ◽  
Facial Emotion

scholarly journals Two-Stage Recognition and beyond for Compound Facial Emotion Recognition

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2847
Author(s):  
Dorota Kamińska ◽  
Kadir Aktas ◽  
Davit Rizhinashvili ◽  
Danila Kuklyanov ◽  
Abdallah Hussein Sham ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Complex Problem ◽  
Facial Emotion Recognition ◽  
Facial Emotion ◽  
Facial Features ◽  
Recognition Method ◽  
Two Stage ◽  
Facial Images ◽  
Emotion Labels

Facial emotion recognition is an inherently complex problem due to individual diversity in facial features and racial and cultural differences. Moreover, facial expressions typically reflect the mixture of people’s emotional statuses, which can be expressed using compound emotions. Compound facial emotion recognition makes the problem even more difficult because the discrimination between dominant and complementary emotions is usually weak. We have created a database that includes 31,250 facial images with different emotions of 115 subjects whose gender distribution is almost uniform to address compound emotion recognition. In addition, we have organized a competition based on the proposed dataset, held at FG workshop 2020. This paper analyzes the winner’s approach—a two-stage recognition method (1st stage, coarse recognition; 2nd stage, fine recognition), which enhances the classification of symmetrical emotion labels.

Real-Time Facial Emotion Recognition

2021 ◽  
Author(s):  
Devanshu Shah ◽  
Khushi Chavan ◽  
Sanket Shah ◽  
Pratik Kanani
Keyword(s):  
Emotion Recognition ◽  
Real Time ◽  
Facial Emotion Recognition ◽  
Facial Emotion

The relationships among facial emotion recognition, social skills, and quality of life

1995 ◽  
Vol 16 (5) ◽  
pp. 383-391 ◽  
Author(s):  
Elliott W. Simon ◽  
Marvin Rosen ◽  
Elliot Grossman ◽  
Edward Pratowski
Keyword(s):  
Quality Of Life ◽  
Social Skills ◽  
Emotion Recognition ◽  
Facial Emotion Recognition ◽  
Facial Emotion
Sign in / Sign up

Export Citation Format

Share Document