Spline-Based Image Registration
International Journal of Computer Vision
Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action Units for Facial Expression Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Expressive expression mapping with ratio images
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Spotting Segments Displaying Facial Expression from Image Sequences Using HMM
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Generalized image matching by the method of differences
Generalized image matching by the method of differences
Automatic recognition of facial expressions using hidden markov models and estimation of expression intensity
Optical Flow Estimation Using Wavelet Motion Model
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Active Appearance Models Revisited
International Journal of Computer Vision
Automatic facial expression recognition based on spatiotemporal descriptors
Pattern Recognition Letters
A multimodal approach for online estimation of subtle facial expression
PCM'12 Proceedings of the 13th Pacific-Rim conference on Advances in Multimedia Information Processing
Computer Vision and Image Understanding
Dynamic facial expression analysis based on extended spatio-temporal histogram of oriented gradients
International Journal of Biometrics
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This paper proposes a novel method for subtle facial expression recognition that uses motion magnification to transform subtle expressions into corresponding exaggerated ones. Motion magnification consists of four steps: First, active appearance model (AAM) fitting extracts 70 facial feature points in the face image sequence. Second, the face image sequence is aligned using the three feature points (two eyes and nose tip). Third, the motion vectors of 27 feature points are estimated using the feature point tracking method. Finally, exaggerated facial expressions are obtained by magnifying the motion vectors of the 27 feature points. After motion magnification, the exaggerated facial expressions are recognized as follows: first, the shape and appearance features are obtained by projecting the exaggerated facial expression image to the AAM shape and appearance model. Second, support vector machines (SVM) are used to classify shape and appearance features. Experimental results show that proposed subtle facial recognition rate is 88.125% for the 80 facial expression images in the SFED2007 database.