Facial action recognition for facial expression analysis from static face images

By Maja Pantic, Léon J. M. Rothkrantz. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2004

471 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/c2e17417bcae6c5ee23599317b0f23a89047ccc1

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In Computer Vision and Pattern Recognition

In Human-Computer Interaction

Citations in the corpus

Recognizing facial expression: machine learning and application to spontaneous behavior (2005, unknown): “Several research groups have recognized the importance of automatically recognizing FACS [3, 16, 14, 8].”
Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks (2017, background): “Earlier research mostly focuses on expression analysis based on still frames [17]–[19].”
Facial Expression Analysis (2011, background): “Approaches that use only geometric features (or their derivatives) mostly rely on detecting sets of fiducial facial points [94, 96, 121], a connected face mesh or ac-”
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships (2007, methodology): “Pantic and Rothkrantz [18] utilize a rule-based method with fast direct chaining inference procedure for AU recognition.”
A 3D facial expression database for facial behavior research (2006, methodology): “Some have successfully used partial 3D information, such as multiple views [29] or 3D models for facial expression analysis [2, 17, 43, 26].”
Measuring Facial Action (2008, background): “While variation due to pose and motion may be eliminated by securing the head in a clamp, as is typically done in neuro-imaging studies, or by wearing a head-mounted camera (Pantic & Rothkrantz 2004), these solutions are not without limitations.”
3D Facial Expression Recognition Based on Primitive Surface Feature Distribution (2006, background): “Some researchers have successfully explored partial 3D information for facial expression recognition, such as multiple-view based [18] and 3D model-based techniques [2, 13, 27, 30].”
Automatic facial expression recognition using features of salient facial patches (2015, unknown): “—————————— ——————————”
Fully Automatic Recognition of the Temporal Phases of Facial Actions (2012, background): “Several promising prototype systems were reported that can recognize deliberately produced AUs in either (near) frontal view [30]–[32] or profile view face images [32], [33] (for a survey of the past work on the topic, see [9] and [10]).”
Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark (2011, unknown): “Furthermore, facial expression analysis methods can also be classified into image based [18, 13] and video based [23, 28].”
Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences (2006, methodology): “The majority of this previous work concerns automatic AU recognition in static face images [7], [25].”
Fully Automatic Facial Action Recognition in Spontaneous Behavior (2006, background): “Several research groups have recognized th e importance of automatically recognizing FACS [1, 10, 8, 5].”
Facial expression recognition based on Local Binary Patterns: A comprehensive study (2009, methodology): “Facial geometry analysis has been widely exploited in facial representation [9,10,16,17,24], where shapes and locations of facial components are extracted to represent the face geometry.”
Automatic Recognition of Facial Actions in Spontaneous Expressions (2006, background): “Several research groups have recognized the importance of automatically coding expressions in terms of FACS [11], [29], [37], [45], [46].”
Face Expression Recognition and Analysis: The State of the Art (2012, methodology): “To address these issues, Pantic and Rothkrantz have worked on the automatic AU coding of profile images [15], [20].”