The MUG facial expression database

By Niki Aifanti, Christos Papachristou, A. Delopoulos. 11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10, 2010

374 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f1af714b92372c8e606485a3982eab2f16772ad8

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Citations in the corpus

MoCoGAN: Decomposing Motion and Content for Video Generation (2017, methodology): “For each combination, we trained a MoCoGAN network using the MUG Facial Expression Database [1].”
CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation (2014, methodology): “The success in conventional facial expression recognition largely rely on sufficient facial expression databases, such as the popularly used CK + [13], MUG [14], MMI [15], JAFFE [16], Multi-PIE [17], and also several 3-”
Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization (2011, background): “Databases with more annotated landmarks such as IMM [23] (58 landmarks, 240 images), MUG Facial Expression Database [22] (80 landmarks for a subset of 401 images) or AR Purdue [18] (22 point markup for 513 images, 130 for 897 images) provide only some hundreds of images.”
Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace (2019, background): “Until recently affective computing has been mostly studied in controlled settings of the environments [14, 25, 39, 47, 48], with limited amount of participants [3, 26, 31, 33, 40], using pre-deﬁned scenarios that users have to follow, depicting posed expressions [1, 28].”