Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Object Classes and Image Synthesis From a Single Example Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
What Is the Set of Images of an Object Under All Possible Illumination Conditions?
International Journal of Computer Vision
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: Features Versus Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Journal of Cognitive Neuroscience
Face Recognition Using Face-ARG Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition from a single image per person: A survey
Pattern Recognition
Interest point detection using imbalance oriented selection
Pattern Recognition
Recognizing faces under facial expression variations and partial occlusions
SIP'08 Proceedings of the 7th WSEAS International Conference on Signal Processing
Illumination Invariant Face Recognition under Various Facial Expressions and Occlusions
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
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
Recognition of expression variant faces from one sample image per enrolled subject
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
An optical flow-based approach to robust face recognition under expression variations
IEEE Transactions on Image Processing
Self-adaptive classifier fusion for expression-insensitive face recognition
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Facial expression analysis using nonlinear decomposable generative models
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
Adaptive discriminant learning for face recognition
Pattern Recognition
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Although important contributions on face recognition have been recently reported, few are focused on how to robustly recognize expression variant faces from as little as one single training sample per class. Since learning cannot generally be applied when only one sample per class is available, matching techniques (distance measures) are usually employed instead (e.g. correlations). However, distance measures generally attempt to match all features with equal importance (weighting), because not only it is difficult to know which features are more useful (for classification), but when or under which circumstances this happens. For example, when recognizing faces in the original image space (e.g. using the Euclidean distance-correlation), it is not known which pixels are more and which are less appropriate to be used. In this contribution, we use the optical flow between the testing and sample images as a measure of how good each pixel is. Pixels that have a small flow will have high weights, pixels with a large flow will have small weights. Our experimental results show that the method proposed in this contribution outperforms the classical Euclidean distance (correlation) measure and the PCA approach.