Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion
International Journal of Computer Vision
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Handbook of Face Recognition
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Fully Automatic Facial Action Recognition in Spontaneous Behavior
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Fully Automatic Facial Action Unit Detection and Temporal Analysis
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Real time 2-D face detection using color ratios and K-mean clustering
Proceedings of the 44th annual Southeast regional conference
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Manifold based analysis of facial expression
Image and Vision Computing
Pose-invariant facial expression recognition using variable-intensity templates
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Real-time capable method for facial expression recognition in color and stereo vision
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
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This work proposes new static and dynamic based methods for facial expression recognition in stereo image sequences. Computer vision 3-d techniques are applied to determine real world geometric measures and to build a static geometric feature vector. Optical flow based motion detection is also carried out which delivers the dynamic flow feature vector. Support vector machine classification is used to recognize the expression using geometric feature vector while k-nearest neighbor classification is used for flow feature vector. The proposed method achieves robust feature detection and expression classification besides covering the in/out of plane head rotations and back and forth movements. Further, a wide range of human skin color is exploited in the training and the test samples.