Learning translation invariant recognition in massively parallel networks
Volume I: Parallel architectures on PARLE: Parallel Architectures and Languages Europe
Numerical recipes in C: the art of scientific computing
Numerical recipes in C: the art of scientific computing
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Slow feature analysis: unsupervised learning of invariances
Neural Computation
The CMU Pose, Illumination, and Expression Database
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Robust Head Pose Estimation Using LGBP
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Rapid convergence to feature layer correspondences
Neural Computation
Distance Measures for Gabor Jets-Based Face Authentication: A Comparative Evaluation
ICB '07 Proceedings of the international conference on Advances in Biometrics
Head Pose Estimation in Computer Vision: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning from Examples to Generalize over Pose and Illumination
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Invariant object recognition and pose estimation with slow feature analysis
Neural Computation
The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Similarity rank correlation for face recognition under unenrolled pose
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
| Hi-index | 0.00 |
Autonomous learning is demonstrated by living beings that learn visual invariances during their visual experience. Standard neural network models do not show this sort of learning. On the example of face recognition in different situations we propose a learning process that separates learning of the invariance proper from learning new instances of individuals. The invariance is learned by a set of examples called model, which contains instances of all situations. New instances are compared with these on the basis of rank lists, which allow generalization across situations. The result is also implemented as a spike-time-based neural network, which is shown to be robust against disturbances. The learning capability is demonstrated by recognition experiments on a set of standard face databases.