A depth video-based facial expression recognition system utilizing generalized local directional deviation-based binary pattern feature discriminant analysis

2015 ◽  
Vol 75 (12) ◽  
pp. 6871-6886 ◽  
Author(s):  
Md. Zia Uddin
Keyword(s):  
Discriminant Analysis ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Expression Recognition ◽  
Depth Video

Automated Facial Expression Recognition System

2009 ◽  
Author(s):  
Andrew Ryan ◽  
Jeffery F. Cohn ◽  
Simon Lucey ◽  
Jason Saragih ◽  
Patrick Lucey ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Expression Recognition

scholarly journals Real Time Facial Expression Recognition System Based on Deep Learning

2019 ◽  
Vol 8 (2S11) ◽  
pp. 4047-4051
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Expression Recognition ◽  
Or Education ◽  
Face Expression ◽  
Active Research ◽  
Main Components

The automatic detection of facial expressions is an active research topic, since its wide fields of applications in human-computer interaction, games, security or education. However, the latest studies have been made in controlled laboratory environments, which is not according to real world scenarios. For that reason, a real time Facial Expression Recognition System (FERS) is proposed in this paper, in which a deep learning approach is applied to enhance the detection of six basic emotions: happiness, sadness, anger, disgust, fear and surprise in a real-time video streaming. This system is composed of three main components: face detection, face preparation and face expression classification. The results of proposed FERS achieve a 65% of accuracy, trained over 35558 face images..

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