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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Jin, Y., Tao, L., Xu, G., & Peng, Y. (2007). A theoretical approach to construct highly discriminative features with application in AdaBoost. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4843 LNCS, pp. 748–757). Springer Verlag. https://doi.org/10.1007/978-3-540-76386-4_71
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