Automatic facial expression recognition in real-time from dynamic sequences of 3D face scans

2013 ◽  
Vol 29 (12) ◽  
pp. 1333-1350 ◽  
Author(s):  
Stefano Berretti ◽  
Alberto del Bimbo ◽  
Pietro Pala
Keyword(s):  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
3D Face

Deep Learning for Real Time Facial Expression Recognition in Social Robots

2018 ◽  
pp. 392-402 ◽  
Author(s):  
Ariel Ruiz-Garcia ◽  
Nicola Webb ◽  
Vasile Palade ◽  
Mark Eastwood ◽  
Mark Elshaw
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Social Robots ◽  
Expression Recognition

scholarly journals Facial Expression Analysis by Machine Learning

2010 ◽  
pp. 239-258
Author(s):  
Siu-Yeung Cho ◽  
Teik-Toe Teoh ◽  
Yok-Yen Nguwi
Keyword(s):  
Feature Selection ◽  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Classification Performance ◽  
Difficult Problem ◽  
Facial Features ◽  
Expression Recognition ◽  
Real Time Processing ◽  
Good Classification Performance

Facial expression recognition is a challenging task. A facial expression is formed by contracting or relaxing different facial muscles on human face that results in temporally deformed facial features like wide-open mouth, raising eyebrows or etc. The challenges of such system have to address with some issues. For instances, lighting condition is a very difficult problem to constraint and regulate. On the other hand, real-time processing is also a challenging problem since there are so many facial features to be extracted and processed and sometimes, conventional classifiers are not even effective in handling those features and produce good classification performance. This chapter discusses the issues on how the advanced feature selection techniques together with good classifiers can play a vital important role of real-time facial expression recognition. Several feature selection methods and classifiers are discussed and their evaluations for real-time facial expression recognition are presented in this chapter. The content of this chapter is a way to open-up a discussion about building a real-time system to read and respond to the emotions of people from facial expressions.

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..

scholarly journals LAFTER: a real-time face and lips tracker with facial expression recognition

2000 ◽  
Vol 33 (8) ◽  
pp. 1369-1382 ◽  
Author(s):  
Nuria Oliver ◽  
Alex Pentland ◽  
François Bérard
Keyword(s):  
Facial Expression ◽  
Real Time ◽  
Facial Expression Recognition ◽  
Expression Recognition
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