Coding facial expressions with Gabor wavelets

By Michael J. Lyons, Shigeru Akamatsu, Miyuki G. Kamachi, Jiro Gyoba. 1998

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Citations in the corpus

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-”
Measuring Facial Action (2008, background): “Most of the work in automatic facial expression recognition has focused on emotionspeciWed expressions, such as joy and anger (Black & Yacoob 1994; Essa & Pentland 1997; Lyons et al. 1998; Padgett & Cottrell 1998; Yacoob & Davis 1997).”
Facial Expression Analysis (2011, background): “Some of the leading efforts include those at: Carnegie Mellon University [110, 80, 106, 141], University of California, San Diego [7, 69], University of Illinois at Urbana-Champaign [23, 127], Rensselaer Polytechnic Institute [115], Massachusetts Institute of Technology [43], University of Maryland ”
MACH: my automated conversation coach (2013, methodology): “In addition, we tested the smile module on the JAFEE 178-image dataset [24] of happiness, sadness, surprise, anger, and disgust, including 29 instances of happy faces from 10 Japanese women.”
Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order (2017, methodology): “Their experiments were conducted using two public databases of static images, Cohn-Kanade [4] and JAFFE [51], and achieved an accuracy of 96.”
Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines (2007, methodology): “…to correctly classify facial expressions and FAUs, taking under consideration only geometrical displacement information based on the standard and well known Candide grid [35], contrary to other approaches that use their own models without having explicitly defined them [7], [9], [23], [36], [37].”
Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments (2008, background): “Japanese Female Facial Expression (JAFFE) Database 10 213 rated for emotional content, female, Japanese [20]”
Evidence and a Computational Explanation of Cultural Differences in Facial Expression Recognition (2010, background): “JAFFE photo sessions, Lyons et al. (1998) also acquired photos of”
Automatic Analysis of Facial Expressions: The State of the Art (2000, background): “Each component of a jet is the filter response of a certain Gabor wavelet [50] extracted at a point of the input image.”
Authentic facial expression analysis (2004, methodology): “Only a few such databases are available, such as the Cohn–Kanade [20] and JAFFE [23] databases.”
Local Higher-Order Statistics (LHS) for Texture Categorization and Facial Analysis (2012, methodology): “Japanese Female Facial Expressions (JAFFE)(3) [15] is a dataset for facial expression recognition.”
Projection functions for eye detection (2004, methodology): “Fig.5 Complementary effect of IPF v and VPF v in capturing the vertical variation Fig.6 HPF v works better than both IPF v and VPF v in capturing the vertical variation been used in the processing of expressions [16].”