Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/43466
Title: Smile detection using local binary patterns and support vector machines
Authors: Freire, D.
Castrillón, M. 
Déniz, O.
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Facial analysis
SVM
K-NN
PCA
LBP
Issue Date: 2009
Conference: 4th International Conference on Computer Vision Theory and Applications 
Abstract: Facial expression recognition has been the subject of much research in the last years within the Computer Vision community. The detection of smiles, however, has received less attention. Its distinctive configuration may pose less problem than other, at times subtle, expressions. On the other hand, smiles can still be very useful as a measure of happiness, enjoyment or even approval. Geometrical or local-based detection approaches like the use of lip edges may not be robust enough and thus researchers have focused on applying machine learning to appearance-based descriptors. This work makes an extensive experimental study of smile detection testing the Local Binary Patterns (LBP) as main descriptors of the image, along with the powerful Support Vector Machines classifier. The results show that error rates can be acceptable, although there is still room for improvement.
URI: http://hdl.handle.net/10553/43466
ISBN: 978-989-8111-69-2
DOI: 10.5220/0001792303980401
Source: VISAPP 2009 - Proceedings of the 4th International Conference on Computer Vision Theory and Applications,v. 1, p. 398-401
Appears in Collections:Actas de congresos
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