Authors:
D. Freire
;
M. Castrillón
and
O. Déniz
Affiliation:
Universidad de Las Palmas de Gran Canaria, Spain
Keyword(s):
Facial analysis, SVM, K-NN, PCA, LBP.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
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.