Abstract
AdaBoost is a practical method of real-time face detection, but abides by a crucial problem of overfitting for the big number of features used in a trained classifier due to the weak discriminative abilities of these features. This paper proposes a theoretical approach to construct highly discriminative features, which is named composed features, from Haar-like features. Both of the composed and Haar-like features are employed to train a multi-view face detector. The primary experiments show promising results in reducing the number of features used in a classifier, which leads to the increase of the generalization ability of the classifier.
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References
Freund, Y., Schapire, R.E.: A Decision- Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Science 55, 119–139 (1997)
Schapire, R., Freund, Y., Bartlett, P., Lee, W.S.: Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods. The Annals of Statistics 26(5), 1651–1686 (1998)
Schapire, R., Singer, Y.: Improved Boosting Algorithms Using Confidence-rated Predictions. Machine Learning 37, 297–336 (1999)
Viola, P., Jones, M.: Rapid Object Detection Using a Boosted Cascade of Simple Features. In: IEEE Conf. Computer Vision and Pattern Recognition, IEEE Computer Society Press, Los Alamitos (2001)
Jones, M., Viola, P.: Fast Multi-view Face Detection. In: TR (2003)
Schneiderman, H., Kanade, T.: A statistical method for 3D object detection applied to faces and cars. In: IEEE Conf. Computer Vision and Pattern Recognition, IEEE Computer Society Press, Los Alamitos (2000)
Huang, C., Ai, H., et al.: Vector Boosting for Rotation Invariant Multi-view Face Detection. In: IEEE International Conf. on Computer Vision (2005)
Wang, Y., Liu, Y., Tao, L., Xu, G.: Real-Time Multi-View Face Detection and Pose Estimation in Video Stream. In: IEEE International Conf. on Pattern Recognition (2006)
Zhang, D., Li, S.Z., et al.: Real-Time Face Detection Using Boosting in Hierarchical Feature Spaces. In: IEEE International Conf. on Pattern Recognition, IEEE Computer Society Press, Los Alamitos (2004)
Yang, P., Shan, S., et al.: Dong Zhang: Face Recognition Using Ada-Boosted Garbor Features. In: IEEE International Conf. on Automatic Face and Gesture Recognition (2004)
Lienhart, R., Maydt, J.: An Extended Set of Haar-like Features for Rapid Object Detection. In: IEEE International Conf. on Image Processing (2002)
Li, S.Z., Zhu, L., et al.: Statistical Learning of Multi-View Face Detection. In: Tistarelli, M., Bigun, J., Jain, A.K. (eds.) ECCV 2002. LNCS, vol. 2359, Springer, Heidelberg (2002)
Fleuret, F.: Fast binary feature selection with conditional mutual information. Journal of Machine Learning Research 5, 1531–1555 (2004)
Levi, K., Weiss, Y.: Learning object detection from a small number of examples: the importance of good features. In: IEEE Conf. Computer Vision and Pattern Recognition, IEEE Computer Society Press, Los Alamitos (2004)
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Jin, Y., Tao, L., Xu, G., Peng, Y. (2007). A Theoretical Approach to Construct Highly Discriminative Features with Application in AdaBoost. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76386-4_71
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DOI: https://doi.org/10.1007/978-3-540-76386-4_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-76385-7
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