Real time facial expression recognition in video using support vector machines
By Philipp Michel, Rana El Kaliouby. International Conference on Multimodal Interaction, 2003
373 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology
View Paper: https://www.semanticscholar.org/paper/7771b255115341d9339e243df0a48fc53ae38394
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Vinay Bettadapura
- Irene Kotsia
- Ioannis Pitas
- Peter Robinson
- Jeremy N. Bailenson
- Rana El Kaliouby
- James J. Gross
- Cendri A. C. Hutcherson
In Experimental and Cognitive Psychology
- James J. Gross
- Jeremy N. Bailenson
- Vinay Bettadapura
- Oliver P. John
- Iris B. Mauss
- N. Yee
- Irene Kotsia
- Ioannis Pitas
Citations in the corpus
- Real-Time Inference of Complex Mental States from Facial Expressions and Head Gestures (2004, methodology): “Likewise, support vector machines have been used to classify feature point displacements compared to a neutral frame, into an emotion class [15].”
- The Effect of Behavioral Realism and Form Realism of Real-Time Avatar Faces on Verbal Disclosure, Nonverbal Disclosure, Emotion Recognition, and Copresence in Dyadic Interaction (2006, background): “These systems sometimes attempt to recognize specific emotions [19] or alternatively attempt to gauge binary states such as general affect [21] .”
- Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines (2007, methodology): “Our system is different from the method proposed in [38] as: • it uses a general and well known model (Candide facial grid) for tracking and information extraction, and not an arbitrary grid that the author chose, not having been properly defined for public use; • a method for FAU recognition and…”
- Real-time classification of evoked emotions using facial feature tracking and physiological responses (2008, methodology): “In the next section, we discuss some of the previous models of detecting facial emotions through computer algorithms.”
- Face Expression Recognition and Analysis: The State of the Art (2012, background): “When it comes to the ease of recognition, it has been shown that, out of the six prototypic expressions, surprise and happiness are the easiest to recognize [37], [14].”