A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rotation Invariant Neural Network-Based Face Detection
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Robust Real-Time Face Detection
International Journal of Computer Vision
FloatBoost Learning and Statistical Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimization Design of Cascaded Classifiers
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Face detection with boosted Gaussian features
Pattern Recognition
Fast Asymmetric Learning for Cascade Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Design of Cascades of Boosted Ensembles for Face Detection
International Journal of Computer Vision
Face detection using simplified Gabor features and hierarchical regions in a cascade of classifiers
Pattern Recognition Letters
Fast computation of scale normalised Gaussian receptive fields
Scale Space'03 Proceedings of the 4th international conference on Scale space methods in computer vision
| Hi-index | 0.00 |
In this paper, we show that a cascade of classifiers using Gaussian derivatives features up to fourth order can be used efficiently to improve the detection performance and robustness as well when compared with the popular approaches using Haar-like features or using Gaussian derivatives of lower order. We also present a new training method that structures the cascade detection so as to use the least expensive derivatives in the initial stages, so as to reduce the overall computational cost of detection. We demonstrate these improvements with experiments using two publicly available datasets (MIT+CMU and FDDB), in the face detection problem, in addition we perform several experiment to show the robustness of Gaussian derivatives when several transformations are presented in the image.