Learning invariance from transformation sequences
Neural Computation
Recognizing Human Facial Expressions From Long Image Sequences Using Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
Convolutional networks for images, speech, and time series
The handbook of brain theory and neural networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Human expression recognition from motion using a radial basis function network architecture
IEEE Transactions on Neural Networks
Using HVS color space and neural network for face detection with various illuminations
CEA'07 Proceedings of the 2007 annual Conference on International Conference on Computer Engineering and Applications
WSEAS Transactions on Computers
On Decomposing an Unseen 3D Face into Neutral Face and Expression Deformations
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Person-similarity weighted feature for expression recognition
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
GAVTASC'11 Proceedings of the 11th WSEAS international conference on Signal processing, computational geometry and artificial vision, and Proceedings of the 11th WSEAS international conference on Systems theory and scientific computation
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Reliable detection of ordinary facial expressions (e.g. smile) despite the variability among individuals as well as face appearance is an important step toward the realization of perceptual user interface with autonomous perception of persons. We describe a rule-based algorithm for robust facial expression recognition combined with robust face detection using a convolutional neural network. In this study, we address the problem of subject independence as well as translation, rotation, and scale invariance in the recognition of facial expression. The result shows reliable detection of smiles with recognition rate of 97.6% for 5600 still images of more than 10 subjects. The proposed algorithm demonstrated the ability to discriminate smiling from talking based on the saliency score obtained from voting visual cues. To the best of our knowledge, it is the first facial expression recognition model with the property of subject independence combined with robustness to variability in facial appearance.