Analyses of a Multimodal Spontaneous Facial Expression Database

2013 ◽  
Vol 4 (1) ◽  
pp. 34-46 ◽  
Author(s):  
Shangfei Wang ◽  
Zhilei Liu ◽  
Zhaoyu Wang ◽  
Guobing Wu ◽  
Peijia Shen ◽  
...  
Keyword(s):  
Facial Expression ◽  
Facial Expression Database

scholarly journals P2-35: The KU Facial Expression Database: A Validated Database of Emotional and Conversational Expressions

i-Perception ◽  
10.1068/if694 ◽  
2012 ◽  
Vol 3 (9) ◽  
pp. 694-694 ◽  
Author(s):  
Haenah Lee ◽  
Ahyoung Shin ◽  
BoRa Kim ◽  
Christian Wallraven
Keyword(s):  
Facial Expression ◽  
Facial Expression Database

Spontaneous facial expression database for academic emotion inference in online learning

2019 ◽  
Vol 13 (3) ◽  
pp. 329-337 ◽  
Author(s):  
Cunling Bian ◽  
Ya Zhang ◽  
Fei Yang ◽  
Wei Bi ◽  
Weigang Lu
Keyword(s):  
Online Learning ◽  
Facial Expression ◽  
Academic Emotion ◽  
Facial Expression Database

scholarly journals GFT Facial Expression Database

2018 ◽  
Author(s):  
Jeffrey M. Girard ◽  
Wen-Sheng Chu ◽  
László A Jeni ◽  
Jeffrey F Cohn ◽  
Fernando De la Torre ◽  
...  
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Interpersonal Communication ◽  
Group Formation ◽  
Interpersonal Behavior ◽  
Facial Expression Analysis ◽  
Video Frames ◽  
Landmark Tracking ◽  
Linear Svm ◽  
Facial Expression Database

Despite the important role that facial expressions play in interpersonal communication and our knowledge that interpersonal behavior is influenced by social context, no currently available facial expression database includes multiple interacting participants. The Sayette Group Formation Task (GFT) database addresses the need for well-annotated video of multiple participants during unscripted interactions. The database includes 172,800 video frames from 96 participants in 32 three-person groups. To aid in the development of automated facial expression analysis systems, GFT includes expert annotations of FACS occurrence and intensity, facial landmark tracking, and baseline results for linear SVM, deep learning, active patch learning, and personalized classification. Baseline performance is quantified and compared using identical partitioning and a variety of metrics (including means and confidence intervals). The highest performance scores were found for the deep learning and active patch learning methods. Learn more at http://osf.io/7wcyz.

scholarly journals DEVELOPMENT AND VALIDATION OF THE KOKORO RESEARCH CENTER (KRC) FACIAL EXPRESSION DATABASE

PSYCHOLOGIA ◽  
2019 ◽  
Vol 61 (4) ◽  
pp. 221-240 ◽  
Author(s):  
Yoshiyuki UEDA ◽  
Masato NUNOI ◽  
Sakiko YOSHIKAWA
Keyword(s):  
Facial Expression ◽  
Research Center ◽  
Development And Validation ◽  
Facial Expression Database
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