A study for the self similarity smile detection
| Data(s) |
08/04/2016
08/04/2016
2009
|
|---|---|
| Resumo |
<p>[EN]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.</p> |
| Identificador |
http://hdl.handle.net/10553/16403 722129 <p><a href="http://dx.doi.org/10.1007/978-3-642-04391-8_13" target="_blank">10.1007/978-3-642-04391-8_13</a></p> |
| Idioma(s) |
eng |
| Direitos |
info:eu-repo/semantics/openAccess |
| Fonte |
<p>Biometric ID Management and Multimodal Communication. Joint COST 2101 and 2102 International Conference. Proceedigns. Berlin: Springer, 2009 (Lecture Notes in Computer Science, ISSN 0302-9743; vol. 5707, pp 97-104) ISBN 978-3-642-04390-1. Online ISBN 978-3-642-04391-8</p> |
| Palavras-Chave | #120304 Inteligencia artificial |
| Tipo |
info:eu-repo/semantics/conferenceObject |
