Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affective computing
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Smile and Laughter Recognition using Speech Processing and Face Recognition from Conversation Video
CW '05 Proceedings of the 2005 International Conference on Cyberworlds
Fully Automatic Facial Action Recognition in Spontaneous Behavior
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
ENCARA2: Real-time detection of multiple faces at different resolutions in video streams
Journal of Visual Communication and Image Representation
Journal of Cognitive Neuroscience
SURF: speeded up robust features
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Hi-index | 0.02 |
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 and self-similarity descriptors. 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.