Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View
IEEE Transactions on Pattern Analysis and Machine Intelligence
Synthesis of Novel Views from a Single Face Image
International Journal of Computer Vision
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
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
Face recognition with one training image per person
Pattern Recognition Letters
Face recognition from one example view
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Component-based LDA Method for Face Recognition with One Training Sample
AMFG '03 Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Enhanced (PC)2 A for face recognition with one training image per person
Pattern Recognition Letters
Recognizing Frontal Face Images Using Hidden Markov Models with One Training Image per Person
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
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
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
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
Journal of Cognitive Neuroscience
Bayes Optimality in Linear Discriminant Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image and Vision Computing
Sparsity preserving discriminant analysis for single training image face recognition
Pattern Recognition Letters
Robust visual similarity retrieval in single model face databases
Pattern Recognition
Selecting discriminant eigenfaces for face recognition
Pattern Recognition Letters
Single image subspace for face recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal 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
Authenticating corrupted face image based on noise model
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture 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
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Neural Networks
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Face recognition from Single Sample per Person (SSPP) is extremely challenging because only one sample is available for each person. While many discriminant analysis methods, such as Fisherfaces and its numerous variants, have achieved great success in face recognition, these methods cannot work in this scenario, because more than one sample per person are needed to calculate the within-class scatter matrix. To address this problem, we propose Adaptive Discriminant Analysis (ADA) in which the within-class scatter matrix of each enrolled subject is inferred using his/her single sample, by leveraging a generic set with multiple samples per person. Our method is motivated from the assumption that subjects who look alike to each other generally share similar within-class variations. In ADA, a limited number of neighbors for each single sample are first determined from the generic set by using kNN regression or Lasso regression. Then, the within-class scatter matrix of this single sample is inferred as the weighted average of the within-class scatter matrices of these neighbors based on the arithmetic mean or Riemannian mean. Finally, the optimal ADA projection directions can be computed analytically by using the inferred within-class scatter matrices and the actual between-class scatter matrix. The proposed method is evaluated on three databases including FERET database, FRGC database and a large real-world passport-like face database. The extensive results demonstrate the effectiveness of our ADA when compared with the existing solutions to the SSPP problem.