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
Reliable face recognition using adaptive and robust correlation filters
Computer Vision and Image Understanding
Why Is Facial Occlusion a Challenging Problem?
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
ViSOM for Dimensionality Reduction in Face Recognition
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Recognizing Partially Occluded Faces from a Single Exemplar Image Per Person
ISA '09 Proceedings of the 3rd International Conference and Workshops on Advances in Information Security and Assurance
Learning a Self-organizing Map Model on a Riemannian Manifold
Proceedings of the 13th IMA International Conference on Mathematics of Surfaces XIII
Face recognition under occlusions and variant expressions with partial similarity
IEEE Transactions on Information Forensics and Security
Image and Vision Computing
Face alignment by minimizing the closest classification distance
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Single image subspace for face recognition
AMFG'07 Proceedings of the 3rd international conference on Analysis and modeling of faces and gestures
Nonlinear dimensionality reduction for face recognition
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Adaptive nonlinear manifolds and their applications to pattern recognition
Information Sciences: an International Journal
Linear and nonlinear dimensionality reduction for face recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Enhanced local texture feature sets for face recognition under difficult lighting conditions
IEEE Transactions on Image Processing
Probabilistic self-organizing maps for continuous data
IEEE Transactions on Neural Networks
Recognition of partially occluded and rotated images with a network of spiking neurons
IEEE Transactions on Neural Networks
A computational intelligence scheme for the prediction of the daily peak load
Applied Soft Computing
Weighted SOM-Face: selecting local features for recognition from individual face image
IDEAL'05 Proceedings of the 6th international conference on Intelligent Data Engineering and Automated Learning
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
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Feature selection for high dimensional face image using self-organizing maps
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Expert Systems with Applications: An International Journal
On nonlinear dimensionality reduction for face recognition
Image and Vision Computing
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Face Recognition System using Discrete Cosine Transform combined with MLP and RBF Neural Networks
International Journal of Mobile Computing and Multimedia Communications
Adaptive discriminant learning for face recognition
Pattern Recognition
Double linear regressions for single labeled image per person face recognition
Pattern Recognition
Local maximal margin discriminant embedding for face recognition
Journal of Visual Communication and Image Representation
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Most classical template-based frontal face recognition techniques assume that multiple images per person are available for training, while in many real-world applications only one training image per person is available and the test images may be partially occluded or may vary in expressions. This paper addresses those problems by extending a previous local probabilistic approach presented by Martinez, using the self-organizing map (SOM) instead of a mixture of Gaussians to learn the subspace that represented each individual. Based on the localization of the training images, two strategies of learning the SOM topological space are proposed, namely to train a single SOM map for all the samples and to train a separate SOM map for each class, respectively. A soft k nearest neighbor (soft k-NN) ensemble method, which can effectively exploit the outputs of the SOM topological space, is also proposed to identify the unlabeled subjects. Experiments show that the proposed method exhibits high robust performance against the partial occlusions and variant expressions.