Expression-invariant face recognition by facial expression transformations
Pattern Recognition Letters
Integrated Expression-Invariant Face Recognition with Constrained Optical Flow
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Expression-invariant face recognition with constrained optical flow warping
IEEE Transactions on Multimedia
2D expression-invariant face recognition with constrained optical flow
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Expression-invariant face recognition with accurate optical flow
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
An optical flow-based approach to robust face recognition under expression variations
IEEE Transactions on Image Processing
Facial expression transformations for expression-invariant face recognition
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
A review of recent advances in 3D ear- and expression-invariant face biometrics
ACM Computing Surveys (CSUR)
Robust 3D face recognition from expression categorisation
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Multibiometric human recognition using 3D ear and face features
Pattern Recognition
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Face recognition is one of the most intensively studied topics in computer vision and pattern recognition. Facial expression, which changes face geometry, usually has an adverse effect on the performance of a face recognition system. On the other hand, face geometry is a useful cue for recognition. Taking these into account, we utilize the idea of separating geometry and texture information in a face image and model the two types of information by projecting them into separate PCA spaces which are specially designed to capture the distinctive features among different individuals. Subsequently, the texture and geometry attributes are re-combined to form a classifier which is capable of recognizing faces with different expressions. Finally, by studying face geometry, we are able to determine which type of facial expression has been carried out, thus build an expression classifier. Numerical validations of the proposed method are given.