Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships

By Yan Tong, Wenhui Liao, Qiang Ji. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007

412 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/d85d1cb39bc719aed46cad1888f09270e78e2756

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In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Learning active facial patches for expression analysis (2012, methodology): “AU-based methods [18, 19] recognize expressions by detecting AUs, but all of them suffer from the difficulties of AU detection.”
Advanced Structured Prediction (2014, background): “, 2009; Valstar and Pantic, 2007). ED is different from and harder than event recognition. ED in continuous time series involves both localization and recognition. Event recognition systems, such as those from Yamato et al. (1992), Brand et al.”
Social signal processing: Survey of an emerging domain (2009, background): “[187]), few studies on discrimination between spontaneous and posed facial gestures like brow actions and smiles based on temporal dynamics of target facial gestures, head and shoulder gestures [195][197], and few studies on multimodal analysis of audio and visual dynamic behaviours for emotion reco”
Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition (2015, background): “The number, size and location of the parts to be registered may vary (e.g. 2 large [146] or 36 small parts [191]).”
Selective Transfer Machine for Personalized Facial Action Unit Detection (2013, methodology): “[30] used Dynamic Bayesian Networks with appearance features to model AU co-occurrence.”
Fully Automatic Recognition of the Temporal Phases of Facial Actions (2012, neutral): “[40].”
Facial Expression Analysis (2011, background): “Extensions of dynamic Bayesian Networks have been a popular approach for expression analysis [115, 104, 22, 121].”
Painful data: The UNBC-McMaster shoulder pain expression archive database (2011, background): “Automated facial expression detection has made great strides over the past decade [1], [2], [3].”
Detecting depression from facial actions and vocal prosody (2009, background): “Recent work by Tong, Liao, and Ji [ 2 ] and Messinger, Chow, and Cohn [13] addresses intra-modal and inter-personal coordination of facial actions.”
The computer expression recognition toolbox (CERT) (2011, background): “The last decade has seen substantial progress in the field of automatic facial expression recognition systems (e.g., [3], [4], [1], [5], [6]).”
Automatic Analysis of Facial Actions: A Survey (2019, methodology): “Approaches include [177], which proposed to use a directed graph, Bayesian networks (BN).”
The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression (2010, background): “Due to its popularity, most key recent advances in the ﬁeld have evaluated their improvements on the CK database [16, 22, 25, 2, 15].”
Prior Knowledge Improves Decoding of Finger Flexion from Electrocorticographic Signals (2011, methodology): “…infers the posteriordistributionof ﬁngerﬂexionbycombiningapriormodel a measurement model (as shown in Figure 2 ). regression/classiﬁcation methods based on Bayesian inference have been adopted in a variety of domains (Viola and Jones,2004; Tong et al., 2007; Yao et al., 2009; Taylor et al., 2010).”