Synthesis of Novel Views from a Single Face Image
International Journal of Computer Vision
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simple hybrid classifier for face recognition with adaptively generated virtual data
Pattern Recognition Letters
Face recognition from one example view
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Robust to Head Pose from One Sample Image
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Face recognition from a single image per person: A survey
Pattern Recognition
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Advanced Pattern Recognition Technologies with Applications to Biometrics
Advanced Pattern Recognition Technologies with Applications to Biometrics
Face recognition across pose: A review
Pattern Recognition
Sparsity preserving discriminant analysis for single training image face recognition
Pattern Recognition Letters
Efficient 3D reconstruction for face recognition
Pattern Recognition
Single image subspace for face recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Encyclopedia of Biometrics
Linear Regression for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Authenticating corrupted face image based on noise model
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Automatic 3D reconstruction for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
A multi-manifold discriminant analysis method for image feature extraction
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
Robust regression for face recognition
Pattern Recognition
Graph optimization for dimensionality reduction with sparsity constraints
Pattern Recognition
Recognition from a single sample per person with multiple SOM fusion
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Robust face detection using local gradient patterns and evidence accumulation
Pattern Recognition
Face recognition in 2D and 2.5D using ridgelets and photometric stereo
Pattern Recognition
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Face Recognition Using Total Margin-Based Adaptive Fuzzy Support Vector Machines
IEEE Transactions on Neural Networks
A Method of Face Recognition Based on Fuzzy c-Means Clustering and Associated Sub-NNs
IEEE Transactions on Neural Networks
Local Linear Discriminant Analysis Framework Using Sample Neighbors
IEEE Transactions on Neural Networks
Orthogonal discriminant vector for face recognition across pose
Pattern Recognition
Sparse representation or collaborative representation: Which helps face recognition?
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Hi-index | 0.01 |
A limited number of available training samples have become one bottleneck of face recognition. In real-world applications, the face image might have various changes owing to varying illumination, facial expression and poses. However, non-sufficient training samples cannot comprehensively convey these possible changes, so it is hard to improve the accuracy of face recognition. In this paper, we propose to exploit the symmetry of the face to generate new samples and devise a representation based method to perform face recognition. The new training samples really reflect some possible appearance of the face. The devised representation based method simultaneously uses the original and new training samples to perform a two-step classification, which ultimately uses a small number of classes that are 'near' to the test sample to represent and classify it and has a similar advantage as the sparse representation method. This method also takes advantages of the score level fusion, which has proven to be very competent and usually performs better than the decision level and feature level fusion. The experimental results show that the proposed method outperforms state-of-the-art face recognition methods including the sparse representation classification (SRC), linear regression classification (LRC), collaborative representation (CR) and two-phase test sample sparse representation (TPTSSR).