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Claudia Lainscsek
Researcher at Salk Institute for Biological Studies
Publications - 46
Citations - 2426
Claudia Lainscsek is an academic researcher from Salk Institute for Biological Studies. The author has contributed to research in topics: Delay differential equation & Dynamical systems theory. The author has an hindex of 15, co-authored 43 publications receiving 2257 citations. Previous affiliations of Claudia Lainscsek include Graz University of Technology & University of California, San Diego.
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Proceedings ArticleDOI
Recognizing facial expression: machine learning and application to spontaneous behavior
Marian Stewart Bartlett,Gwen Littlewort,Mark G. Frank,Claudia Lainscsek,Ian Fasel,Javier R. Movellan +5 more
TL;DR: The system operates in real-time, and obtained 93% correct generalization to novel subjects for a 7-way forced choice on the Cohn-Kanade expression dataset, and has a mean accuracy of 94.8%.
Journal ArticleDOI
Automatic Recognition of Facial Actions in Spontaneous Expressions
Marian Stewart Bartlett,Gwen Littlewort,Mark G. Frank,Claudia Lainscsek,Ian Fasel,Javier R. Movellan +5 more
TL;DR: A user independent fully automatic system for real time recognition of facial actions from the Facial Action Coding System (FACS) automatically detects frontal faces in the video stream and coded each frame with respect to 20 Action units.
Fully Automatic Facial Action Recognition in Spontaneous Behavior.
Marian Stewart Bartlett,Gwen Littlewort,Mark G. Frank,Claudia Lainscsek,Ian Fasel,Javier R. Movellan +5 more
TL;DR: A user independent fully automatic system for real time recognition of facial actions from the facial action coding system (FACS) and preliminary results on a task of facial action detection in spontaneous expressions during discourse are presented.
Proceedings ArticleDOI
Fully Automatic Facial Action Recognition in Spontaneous Behavior
Marian Stewart Bartlett,Gwen Littlewort,Mark G. Frank,Claudia Lainscsek,Ian Fasel,Javier R. Movellan +5 more
TL;DR: In this paper, a user-independent fully automatic system for real-time recognition of facial actions from the Facial Action Coding System (FACS) was presented, which automatically detects frontal faces in the video stream and codes each frame with respect to 20 Action units.
Proceedings ArticleDOI
Machine learning methods for fully automatic recognition of facial expressions and facial actions
TL;DR: A systematic comparison of machine learning methods applied to the problem of fully automatic recognition of facial expressions reports results on a series of experiments comparing recognition engines, including AdaBoost, support vector machines, linear discriminant analysis, as well as feature selection techniques.