Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Facial Expression Decomposition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Facial asymmetry quantification for expression invariant human identification
Computer Vision and Image Understanding - Special issue on Face recognition
Separating Style and Content with Bilinear Models
Neural Computation
Facial expressional image synthesis controlled by emotional parameters
Pattern Recognition Letters
Expression-Invariant Face Recognition with Expression Classification
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
A Real-Time Facial Expression Recognition using the STAAM
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
On bending invariant signatures for surfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination-robust face recognition using ridge regressive bilinear models
Pattern Recognition Letters
Expression-invariant face recognition by facial expression transformations
Pattern Recognition Letters
Robust real-time face detection using face certainty map
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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This paper presents a method of expression-invariant face recognition by transforming the input face image with an arbitrary expression into its corresponding neutral facial expression image. When a new face image with an arbitrary expression is queried, it is represented by a feature vector using the active appearance model. Then, the facial expression state of the queried feature vector is identified by the facial expression recognizer. Then, the queried feature vector is transformed into the neutral facial expression vector using the identified facial expression state via the direct or indirect facial expression transformation, where the former uses the bilinear translation directly to transform the facial expression, but the latter uses the bilinear translation to obtain the relative expression parameters and transforms the facial expression indirectly by the obtained relative expression parameters. Then, the neutral facial expression vector is converted into the neutral facial expression image via the AAM reconstruction. Finally, the face recognition has been performed by the distance-based matching technique. Experimental results show that the proposed expression-invariant face recognition method is very robust under a variety of facial expressions.