Induced Disgust , Happiness and Surprise : an Addition to the MMI Facial Expression Database

By Michel François Valstar, Maja Pantic. 2010

496 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/aeeea6eec2f063c006c13be865cec0c350244e5b

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Citations in the corpus

Aff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge (2017, methodology): “Some representative datasets, which are still used in many recent works [18], include the Cohn-Kanade database [33, 24], the MMI database [29, 35], the Multi-PIE database [17] and the BU-3D and BU-4D databases [41, 40].”
Painful data: The UNBC-McMaster shoulder pain expression archive database (2011, background): “From initial focus on posed images recorded from a frontal view, current efforts extend to posed data recorded from multiple views [4], [5], 3D imaging [6], and, increasingly, non-posed facial behavior in real-world settings in which non-frontal pose and head motion are common [7], [8].”
A Multimodal Database for Affect Recognition and Implicit Tagging (2012, background): “Pantic et al. created the MMI web-based emotional database of posed and spontaneous facial expressions with both static images and videos [5], [10].”
The first facial expression recognition and analysis challenge (2011, methodology): “Examples of this are the MMI Facial Expression database [18], as well as the Cohn-Kanade database [6].”
Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition (2014, methodology): “The proposed method is evaluated on four public expression databases: CK+ [19], MMI [26], Oulu-CASIA VIS [33], AFEW [6], including both lab-controlled and wild scenarios.”
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-”
A Novel Space-Time Representation on the Positive Semidefinite Cone for Facial Expression Recognition (2017, methodology): “MMI database [37] – consists of 205 image sequences with frontal faces of only 30 subjects labeled with the six basic emotion labels.”
Deeply Learning Deformable Facial Action Parts Model for Dynamic Expression Analysis (2014, methodology): “The number of samples for each expression in CK+ and MMI are illustrated in Table 1.”
Deep Facial Expression Recognition: A Survey (2018, background): “For instance, comparison TABLE 6 Comparison of Different Types of Methods for Dynamic Image Sequences in Terms of Data Size Requirement, Representability of Spatial and Temporal Information, Requirement on Frame Length, Performance, and Computational Efficiency FLT = Facial Landmark Trajectory; CN =”
Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks (2017, methodology): “In order to extract more powerful spatio-temporal features, Liu et al. [6] propose an expressionlet-based spatio-temporal mainfold descriptor which outperforms the previous traditional methods on the CK + [26] and MMI databases [27].”
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].”
Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond (2018, methodology): “still used in many recent works [26], are the Cohn-Kanade database [32,52], MMI database [39,56], Multi-PIE database [22] and the BU-3D and BU-4D databases [61, 62].”
Suppressing Uncertainties for Large-Scale Facial Expression Recognition (2020, background): “…have made signiﬁcant progress on facial expression recognition (FER) with algorithms and large-scale datasets, where datasets can be collected in lab-∗ oratory or in the wild, such as CK+ [29], MMI [39], Oulu-CASIA [47], SFEW/AFEW [10], FERPlus [4], AffectNet [32], EmotioNet [11], RAF-DB [22], etc.”
Automatic Analysis of Facial Actions: A Survey (2019, background): “when basically only the Cohn-Kanade and MMI databases were available [92], [190], lack of high-quality data remains a major issue.”