Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction.

By M. Bartlett, Gwen Littlewort, Ian R. Fasel, Javier R. Movellan. 2003 Conference on Computer Vision and Pattern Recognition Workshop, 2003

601 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/a3dc3353c5c916a865eb3477f739cc21142430fd

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In Experimental and Cognitive Psychology

Citations in the corpus

FaceTracer: A Search Engine for Large Collections of Images with Faces (2008, methodology): “Finally, we note that this is an active area of research, and there are many other works on attribute classification which use different combinations of learning techniques, features, and problem formulations [11,12].”
The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions (2014, background): “…to the more thoroughly studied domains of automatic basic emotion recognition (happy, sad, angry, disgusted, fearful, surprised, or neutral) [6] , [33], [61] or facial action unit classification [7], [27], [37], [47] ), affective states that are relevant to learning such as frustration or…”
Computer vision and deep learning techniques for pedestrian detection and tracking: A survey (2018, background): “…cars demonstrated by the huge investments made by the biggest automotive and IT companies [1] , as well as the development of machines and applications able to interact with persons [2–13] , is playing an important role in the improvement of the techniques for vision-based pedestrian tracking.”
OpenFace: An open source facial behavior analysis toolkit (2016, methodology): “As the face is a very important channel of nonverbal communication [20, 18], facial behavior analysis has been used in different applications to facilitate human computer interaction [10, 43, 48, 66].”
Facial Expression Analysis (2011, background): “Applications of facial expression analysis include marketing [105], perceptual user interfaces, human-robot interaction [124, 142, 98], drowsy driver detection [126], telenursing [29], pain assessment [79], analyzing mother-infant interaction [45], autism [83], social robotics [6, 18], facial animat”
Attribute and simile classifiers for face verification (2009, background): “This was later extended to the recognition of ethnicity [32], pose [20], expression [1], etc.”
Facial Expression Recognition Based on Deep Evolutional Spatial-Temporal Networks (2017, background): “Earlier research mostly focuses on expression analysis based on still frames [17], [18], [19].”
Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines (2007, background): “The system proposed in [8] automatically detects frontal faces in the video stream and classifies them in seven classes in real time: neutral, anger, disgust, fear, joy, sadness, and surprise.”
Cross-dataset learning and person-specific normalisation for automatic Action Unit detection (2015, methodology): “As the face is considered the main channel of nonverbal communication, facial expression analysis is used in different applications to facilitate human computer interaction [3], [17].”