Abstract
In this paper, we propose a novel learning method for face detection using discriminative feature selection. The main deficiency of the boosting algorithm for face detection is its long training time. Through statistical learning theory, our discriminative feature selection method can make the training process for face detection much faster than the boosting algorithm without degrading the generalization performance. Being different from the boosting algorithm which works in an iterative learning way, our method can directly solve the learning problem of face detection. Our method is a novel ensemble learning method for combining multiple weak classifiers. The most discriminative component classifiers are selected for the ensemble. Our experiments show that the proposed discriminative feature selection method is more efficient than the boosting algorithm for face detection.
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© 2006 Springer-Verlag Berlin Heidelberg
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Fan, ZG., Lu, BL. (2006). Fast Learning for Statistical Face Detection. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_21
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DOI: https://doi.org/10.1007/11893257_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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