Temporal Facial Expression Modeling for Automated Action Unit Intensity Measurement

2014 ◽  
Author(s):  
S. Mohammad Mavadati ◽  
Mohammad H. Mahoor
Keyword(s):  
Facial Expression ◽  
Intensity Measurement ◽  
Action Unit ◽  
Facial Expression Modeling

scholarly journals Action unit detection in 3D facial videos with application in facial expression retrieval and recognition

2018 ◽  
Vol 77 (19) ◽  
pp. 24813-24841 ◽  
Author(s):  
Antonios Danelakis ◽  
Theoharis Theoharis ◽  
Ioannis Pratikakis
Keyword(s):  
Facial Expression ◽  
Action Unit ◽  
Action Unit Detection

Learning person-specific models for facial expression and action unit recognition

2013 ◽  
Vol 34 (15) ◽  
pp. 1964-1970 ◽  
Author(s):  
Jixu Chen ◽  
Xiaoming Liu ◽  
Peter Tu ◽  
Amy Aragones
Keyword(s):  
Facial Expression ◽  
Action Unit

scholarly journals Mathematical Representations of Blended Facial Expressions towards Facial Expression Modeling

2016 ◽  
Vol 84 ◽  
pp. 94-98 ◽  
Author(s):  
Priya Saha ◽  
Debotosh Bhattacharjee ◽  
Barin Kumar De ◽  
Mita Nasipuri
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Mathematical Representations ◽  
Facial Expression Modeling

scholarly journals Adaptive 3D Model-Based Facial Expression Synthesis and Pose Frontalization

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2578
Author(s):  
Yu-Jin Hong ◽  
Sung Eun Choi ◽  
Gi Pyo Nam ◽  
Heeseung Choi ◽  
Junghyun Cho ◽  
...  
Keyword(s):  
Face Recognition ◽  
Facial Expression ◽  
Facial Expressions ◽  
Data Augmentation ◽  
Synthesis Method ◽  
Qualitative Evaluation ◽  
Emotional States ◽  
Coding System ◽  
Contour Fitting ◽  
Facial Expression Modeling

Facial expressions are one of the important non-verbal ways used to understand human emotions during communication. Thus, acquiring and reproducing facial expressions is helpful in analyzing human emotional states. However, owing to complex and subtle facial muscle movements, facial expression modeling from images with face poses is difficult to achieve. To handle this issue, we present a method for acquiring facial expressions from a non-frontal single photograph using a 3D-aided approach. In addition, we propose a contour-fitting method that improves the modeling accuracy by automatically rearranging 3D contour landmarks corresponding to fixed 2D image landmarks. The acquired facial expression input can be parametrically manipulated to create various facial expressions through a blendshape or expression transfer based on the FACS (Facial Action Coding System). To achieve a realistic facial expression synthesis, we propose an exemplar-texture wrinkle synthesis method that extracts and synthesizes appropriate expression wrinkles according to the target expression. To do so, we constructed a wrinkle table of various facial expressions from 400 people. As one of the applications, we proved that the expression-pose synthesis method is suitable for expression-invariant face recognition through a quantitative evaluation, and showed the effectiveness based on a qualitative evaluation. We expect our system to be a benefit to various fields such as face recognition, HCI, and data augmentation for deep learning.

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