Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolutionary Pursuit and Its Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition with one training image per person
Pattern Recognition Letters
Beyond Eigenfaces: Probabilistic Matching for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced (PC)2 A for face recognition with one training image per person
Pattern Recognition Letters
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Is ICA Significantly Better than PCA for Face Recognition?
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Preliminary Face Recognition Grand Challenge Results
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition from a single image per person: A survey
Pattern Recognition
On solving the face recognition problem with one training sample per subject
Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Bayes Decision Rule Induced Similarity Measures
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Dimensionality of Face Space
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selecting discriminant eigenfaces for face recognition
Pattern Recognition Letters
Fusing Gabor and LBP feature sets for kernel-based face recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Robust discriminant analysis of latent semantic feature for text categorization
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Gabor-Eigen-Whiten-Cosine: a robust scheme for face recognition
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Face Recognition Algorithms Surpass Humans Matching Faces Over Changes in Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
The small sample size problem of ICA: A comparative study and analysis
Pattern Recognition
Towards collaborative feature extraction for face recognition
Natural Computing: an international journal
Adaptive discriminant learning for face recognition
Pattern Recognition
Hi-index | 0.01 |
Current face recognition techniques rely heavily on the large size and representativeness of the training sets, and most methods suffer degraded performance or fail to work if there is only one training sample per person available. This so-called ''one sample problem'' is a challenging issue in face recognition. In this paper, we propose a novel feature extraction method named uniform pursuit to address the one sample problem. The underlying idea is that most recognition errors are due to the confusions between faces that look very similar, and thus one can reduce the risk of recognition error by mapping the close class prototypes to be distant, i.e., uniforming the pairwise distances between different class prototypes. Specifically, the UP method pursues, in the whitened PCA space, the low dimensional projections that reduce the local confusion between the similar faces. The resulting low dimensional transformed features are robust against the complex image variations such as those caused by lighting and aging. A standardized procedure on the large-scale FERET and FRGC databases is applied to evaluate the one sample problem. Experimental results show that the robustness, accuracy and efficiency of the proposed UP method compare favorably to the state-of-the-art one sample based methods.