A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Detecting Faces in Images: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Robust Real-Time Face Detection
International Journal of Computer Vision
Real-Time Face Detection Using Boosting in Hierarchical Feature Spaces
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Boosting the Minimum Margin: LPBoost vs. AdaBoost
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
A method for optimal division of data sets for use in neural networks
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part IV
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
In this paper we propose to enhance the training data of boosting-based object detection frameworks by the use of principal component analysis (PCA). The quality of boosted classifiers highly depends on the image databases exploited in training. We observed that negative training images projected into the objects PCA space are often far away from the object class. This broad boundary between the object classes in training can yield to a high classification error of the boosted classifier in the testing phase. We show that transforming the negative training database close to the positive object class can increase the detection performance. In experiments on face detection and the analysis of microscopic cell images, our method decreases the amount of false positives while maintaining a high detection rate. We implemented our approach in a Viola & Jones object detection framework using AdaBoost to combine Haar-like features. But as a preprocessing step our method can easily be integrated in all boosting-based frameworks without additional overhead.