Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/16403
Title: A study for the self similarity smile detection
Authors: Freire-Obregón, David
Antón Canalís, Luis
Castrillón, Modesto 
UNESCO Clasification: 120304 Inteligencia artificial
Keywords: Classification
Issue Date: 2009
Journal: Lecture Notes in Computer Science 
Conference: Joint International Conference on Biometric ID Management and Multimodal Communication (BioID-MultiComm) 
Joint COST 2101 and 2102 International Conference on Biometric ID Management and Multimodal Communication, BioID_MultiComm 2009 
Abstract: This work makes an extensive experimental study of smile detection testing the Local Binary Patterns (LBP) combined with self similarity (LAC) as main descriptors of the image, along with the powerful Support Vector Machines classifier. Results show that error rates can be acceptable and the self similarity approach for the detection of smiles is suitable for real-time interaction, although there is still room for improvement.
URI: http://hdl.handle.net/10553/16403
ISBN: 978-3-642-04390-1
3642043909
ISSN: 0302-9743
DOI: 10.1007/978-3-642-04391-8_13
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)[ISSN 0302-9743],v. 5707 LNCS, p. 97-104
Appears in Collections:Actas de congresos
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