MobileEmotiFace: Efficient Facial Image Representations in Video-Based Emotion Recognition on Mobile Devices

2021 ◽  
pp. 266-274
Author(s):  
Polina Demochkina ◽  
Andrey V. Savchenko
Keyword(s):  
Emotion Recognition ◽  
Mobile Devices ◽  
Facial Image ◽  
Image Representations

scholarly journals Facial Image based Emotion Recognition System using Neural Network

IJARCCE ◽  
2017 ◽  
Vol 6 (5) ◽  
pp. 52-66
Author(s):  
Parnal Dudul ◽  
Prof. Tayade S. M. ◽  
Prof. Ajay Talele
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Recognition System ◽  
Facial Image

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.

Emotion Recognition in Intelligent Tutoring Systems for Android-Based Mobile Devices

2014 ◽  
pp. 494-504 ◽  
Author(s):  
Ramón Zatarain-Cabada ◽  
María Lucía Barrón-Estrada ◽  
Giner Alor-Hernández ◽  
Carlos A. Reyes-García
Keyword(s):  
Emotion Recognition ◽  
Mobile Devices ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems

scholarly journals Facial Image Analysis Algorithm for Emotion Recognition

2004 ◽  
Vol 14 (7) ◽  
pp. 801-806 ◽  
Author(s):  
Y.H. Joo ◽  
K.H. Jeong ◽  
M.H. Kim ◽  
J.B. Park ◽  
J. Lee ◽  
...  
Keyword(s):  
Image Analysis ◽  
Emotion Recognition ◽  
Facial Image ◽  
Analysis Algorithm ◽  
Image Analysis Algorithm ◽  
Facial Image Analysis

scholarly journals Emotion Recognition Method of Facial Image using PCA

2006 ◽  
Vol 16 (6) ◽  
pp. 772-776 ◽  
Author(s):  
Ho-Duck Kim ◽  
Hyun-Chang Yang ◽  
Chang-Hyun Park ◽  
Kwee-Bo Sim
Keyword(s):  
Emotion Recognition ◽  
Facial Image ◽  
Recognition Method
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