Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Active shape models—their training and application
Computer Vision and Image Understanding
Recognizing Human Facial Expressions From Long Image Sequences Using Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A 3D Facial Expression Database For Facial Behavior Research
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Expression-Invariant Face Recognition with Expression Classification
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Accurate optical flow computation under non-uniform brightness variations
Computer Vision and Image Understanding
Expression-invariant face recognition with accurate optical flow
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Recognizing expression variant faces from a single sample image per class
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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Face recognition is one of the most intensively studied topics in computer vision and pattern recognition, but few are focused on how to robustly recognize faces with expressions under the restriction of one single training sample per class. A constrained optical flow algorithm, which combines the advantages of the unambiguous correspondence of feature point labeling and the flexible representation of optical flow computation, has been developed for face recognition from expressional face images. In this paper, we propose an integrated face recognition system that is robust against facial expressions by combining information from the computed intraperson optical flow and the synthesized face image in a probabilistic framework. Our experimental results show that the proposed system improves the accuracy of face recognition from expressional face images.