scholarly journals Real-time facial action unit intensity prediction with regularized metric learning

2016 ◽  
Vol 52 ◽  
pp. 1-14 ◽  
Author(s):  
Jérémie Nicolle ◽  
Kévin Bailly ◽  
Mohamed Chetouani
Keyword(s):  
Real Time ◽  
Metric Learning ◽  
Action Unit ◽  
Facial Action ◽  
Intensity Prediction

Joint Facial Action Unit Intensity Prediction And Region Localisation

2020 ◽  
Author(s):  
Yachun Fan ◽  
Jie Shen ◽  
Housen Cheng ◽  
Feng Tian
Keyword(s):  
Action Unit ◽  
Facial Action ◽  
Intensity Prediction

Facial Action Unit Intensity Detection by Extracting Complimentary Information using Distance Metric Learning

2018 ◽  
Vol 66 (2) ◽  
pp. 214-223 ◽  
Author(s):  
Neeru Rathee ◽  
Dinesh Ganotra ◽  
Ajay Rathee
Keyword(s):  
Metric Learning ◽  
Distance Metric Learning ◽  
Distance Metric ◽  
Action Unit ◽  
Facial Action

Exploration of real-time satellite measurements to advance hurricane intensity prediction in the northern Gulf of Mexico

OCEANS 2009 ◽  
2009 ◽  
Author(s):  
Nan Walker ◽  
Robert Leben ◽  
Steven Anderson ◽  
Alaric Haag ◽  
Chet Pilley ◽  
...  
Keyword(s):  
Gulf Of Mexico ◽  
Real Time ◽  
Satellite Measurements ◽  
Hurricane Intensity ◽  
Northern Gulf Of Mexico ◽  
Intensity Prediction

Tunneled Latent Variables Method for Facial Action Unit Tracking

2009 ◽  
Vol 35 (2) ◽  
pp. 198-201 ◽  
Author(s):  
Lei WANG ◽  
Bei-Ji ZOU ◽  
Xiao-Ning PENG
Keyword(s):  
Latent Variables ◽  
Action Unit ◽  
Facial Action

Multi-view facial action unit detection via DenseNets and CapsNets

2021 ◽  
Author(s):  
Dakai Ren ◽  
Xiangmin Wen ◽  
Jiazhong Chen ◽  
Yu Han ◽  
Shiqi Zhang
Keyword(s):  
Action Unit ◽  
Facial Action ◽  
Action Unit Detection

scholarly journals Assessing Automated Facial Action Unit Detection Systems for Analyzing Cross-Domain Facial Expression Databases

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4222
Author(s):  
Shushi Namba ◽  
Wataru Sato ◽  
Masaki Osumi ◽  
Koh Shimokawa
Keyword(s):  
Affective Computing ◽  
Detection System ◽  
Characteristic Curve ◽  
Unmet Need ◽  
Systematic Evaluation ◽  
Dynamic Mode ◽  
Coding System ◽  
Static Mode ◽  
Action Unit ◽  
Facial Action

In the field of affective computing, achieving accurate automatic detection of facial movements is an important issue, and great progress has already been made. However, a systematic evaluation of systems that now have access to the dynamic facial database remains an unmet need. This study compared the performance of three systems (FaceReader, OpenFace, AFARtoolbox) that detect each facial movement corresponding to an action unit (AU) derived from the Facial Action Coding System. All machines could detect the presence of AUs from the dynamic facial database at a level above chance. Moreover, OpenFace and AFAR provided higher area under the receiver operating characteristic curve values compared to FaceReader. In addition, several confusion biases of facial components (e.g., AU12 and AU14) were observed to be related to each automated AU detection system and the static mode was superior to dynamic mode for analyzing the posed facial database. These findings demonstrate the features of prediction patterns for each system and provide guidance for research on facial expressions.

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