Facial Expression Recognition (FER) is a challenging
task because many factors of variation such as pose,
illumination and identity-specific attributes are entangled with
the expression information in an expressive face image. Recent
works show that the performance of a FER algorithm
can be improved by disentangling the expression information
from identity features. In this paper, we present Transferbased
Expression Recognition Generative Adversarial Network
(TER-GAN) that combines the effectiveness of a novel feature
disentanglement technique with the concept of identity-invariant
expression representation learning for FER. More specifically,
TER-GAN learns a disentangled expression representation by
extracting expression features from one image and transferring
the expression information to the identity of another image. To
improve the feature disentanglement process and to learn an
identity-invariant expression representation, we introduce a novel
expression consistency loss and an identity consistency loss that
exploit expression and identity information from both real and
synthetic images. We evaluated the performance of our proposed
facial expression recognition technique by employing four public
facial expression databases, CK+, Oulu-CASIA, MMI, BU-3DFE.
The experimental results show the effectiveness of the proposed
technique.

Facial Expression Recognition By Using a Disentangled Identity-Invariant Expression
Representation

PS T2.1: Biometrics and Human Analysis

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


Kamran Ali

1

SHORT BIO

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Facial Expression Recognition By Using a Disentangled Identity-Invariant Expression Representation

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