Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
An eigenspace update algorithm for image analysis
Graphical Models and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental PCA or On-Line Visual Learning and Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Robust Real-Time Face Detection
International Journal of Computer Vision
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
An ensemble-based method for linear feature extraction for two-class problems
Pattern Analysis & Applications
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Chunk incremental LDA computing on data streams
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
| Hi-index | 0.00 |
In real life, visual learning is supposed to be a continuous process. Humans have an innate facility to recognize objects even under less-than-ideal conditions and to build robust representations of them. These representations can be altered with the arrival of new information and thus the model of the world is continuously updated. Inspired by the biological paradigm, we propose in this paper an incremental subspace representation for cognitive vision processes. The proposed approach has been applied to the problem of face recognition. The experiments performed on a custom database show that at the end of incremental learning process the recognition performance achieved converges towards the result obtained using an off-line learning strategy.