Recognizing Human Facial Expressions From Long Image Sequences Using Optical Flow
IEEE Transactions on Pattern Analysis and Machine Intelligence
SUSAN—A New Approach to Low Level Image Processing
International Journal of Computer Vision
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Interpretation and Coding of Face Images Using Flexible Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coding, Analysis, Interpretation, and Recognition of Facial Expressions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Classification of Single Facial Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Interest Point Detectors
International Journal of Computer Vision - Special issue on a special section on visual surveillance
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Matrix Analysis and Applications
Smiling Faces are Better for Face Recognition
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Multiple-Exemplar Discriminant Analysis for Face Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
An Axiomatic Approach to Corner Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Multi-Image Matching Using Multi-Scale Oriented Patches
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Identity Representability of Facial Expressions: An Evaluation Using Feature Pixel Distributions
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Journal of Cognitive Neuroscience
Random sampling LDA for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Probabilistic expression analysis on manifolds
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The study on how to represent appearance instances was the focus in most previous work in face recognition. Little attention, however, was given to the problem of how to select ''good'' instances for a gallery, which may be called the facial identity representation problem. This paper gives an evaluation of the identity representability of facial expressions. The identity representability of an expression is measured by the recognition accuracy achieved by using its samples as the gallery data. We use feature distributions to represent appearance instances. A feature distribution of an image is based on the number of occurrence of detected interest points in regular grids of an image plane. We present a new algorithm of imbalance oriented candidate selection for interest point detection. Our experimental evaluation indicates that certain facial expressions, such as the neutral, have stronger identity representability than other expressions, in various feature distributions. An application of evaluation results towards improving linear discriminant analysis is further presented to show the value of our evaluation work.