Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
Soft Computing and Fuzzy Logic
IEEE Software
Improving Regressors using Boosting Techniques
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Best Practices for Convolutional Neural Networks Applied to Visual Document Analysis
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
An introduction to variable and feature selection
The Journal of Machine Learning Research
Experiments with AdaBoost.RT, an improved boosting scheme for regression
Neural Computation
Head Pose estimation on low resolution images
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Evaluation of head pose estimation for studio data
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
Neural network-based head pose estimation and multi-view fusion
CLEAR'06 Proceedings of the 1st international evaluation conference on Classification of events, activities and relationships
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Boost feature subset selection: a new gene selection algorithm for microarray dataset
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
Feature selection using dynamic weights for classification
Knowledge-Based Systems
| Hi-index | 0.00 |
The head pose estimation problem is well known to be a challenging task in computer vision and is a useful tool for several applications involving human-computer interaction. This problem can be stated as a regression one where the input is an image and the output is pan and tilt angles. Finding the optimal regression is a hard problem because of the high dimensionality of the input (number of image pixels) and the large variety of morphologies and illumination. We propose a new method combining a boosting strategy for feature selection and a neural network for the regression. Potential features are a very large set of Haar-like wavelets which are well known to be adapted to face image processing. To achieve the feature selection, a new Fuzzy Functional Criterion (FFC) is introduced which is able to evaluate the link between a feature and the output without any estimation of the joint probability density function as in the Mutual Information. The boosting strategy uses this criterion at each step: features are evaluated by the FFC using weights on examples computed from the error produced by the neural network trained at the previous step. Tests are carried out on the commonly used Pointing 04 database and compared with three state-of-the-art methods. We also evaluate the accuracy of the estimation on FacePix, a database with a high angular resolution. Our method is compared positively to a Convolutional Neural Network, which is well known to incorporate feature extraction in its first layers.