The nature of statistical learning theory
The nature of statistical learning theory
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Classifier Conditional Posterior Probabilities
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
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) - Workshops - Volume 03
A review on Gabor wavelets for face recognition
Pattern Analysis & Applications
Journal of Cognitive Neuroscience
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Alternatives to parameter selection for kernel methods
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
IEEE Transactions on Image Processing
An introduction to biometric recognition
IEEE Transactions on Circuits and Systems for Video Technology
Kernel fusion of multiple histogram descriptors for robust face recognition
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Hi-index | 0.10 |
In this paper, the fusion of information from several data representations in a face verification environment has been analyzed. We have considered three different face-based biometric representations of each subject in a subset of the Face Recognition Grand Challenge (FRGC) database: 2D texture images, 2.5D range data and mean curvature images. From these representations, linear and Gaussian kernel matrices have been defined. Fusion techniques have been applied to obtain a unique kernel from the individual kernels. The resulting kernel has been used to train Support Vector Machines (SVMs) for verification tasks. The proposed classifier outperforms the individual kernels results and the results of classical fusion techniques (feature-level and score-level methods) in different security level systems.