Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning - Special issue on inductive transfer
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Merging and Splitting Eigenspace Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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
An eigenspace update algorithm for image analysis
ISCV '95 Proceedings of the International Symposium on Computer Vision
Online ensemble learning
Nonparametric discriminant analysis and nearest neighbor classification
Pattern Recognition Letters
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Online Detection and Classification of Moving Objects Using Progressively Improving Detectors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
The 2005 IEEE Workshop on Face Recognition Grand Challenge Experiments
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Boosted discriminant projections for nearest neighbor classification
Pattern Recognition
Incremental learning of feature space and classifier for on-line pattern recognition
International Journal of Knowledge-based and Intelligent Engineering Systems - Innovational Soft Computing
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Journal of Cognitive Neuroscience
An ensemble-based method for linear feature extraction for two-class problems
Pattern Analysis & Applications
A model of inductive bias learning
Journal of Artificial Intelligence Research
Sharing features: efficient boosting procedures for multiclass object detection
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
On-Line, incremental learning of a robust active shape model
DAGM'06 Proceedings of the 28th conference on Pattern Recognition
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Individual identification using personality traits
Journal of Network and Computer Applications
Robust classifiers for data reduced via random projections
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Journal of Real-Time Image Processing
Visual-context boosting for eye detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
Face recognition applications commonly suffer from three main drawbacks: a reduced training set, information lying in high-dimensional subspaces, and the need to incorporate new people to recognize. In the recent literature, the extension of a face classifier in order to include new people in the model has been solved using online feature extraction techniques. The most successful approaches of those are the extensions of the principal component analysis or the linear discriminant analysis. In the current paper, a new online boosting algorithm is introduced: a face recognition method that extends a boosting-based classifier by adding new classes while avoiding the need of retraining the classifier each time a new person joins the system. The classifier is learned using the multitask learning principle where multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the structure learned previously, being the addition of new classes not computationally demanding. The present proposal has been (experimentally) validated with two different facial data sets by comparing our approach with the current state-of-the-art techniques. The results show that the proposed online boosting algorithm fares better in terms of final accuracy. In addition, the global performance does not decrease drastically even when the number of classes of the base problem is multiplied by eight.