Abstract
A new boosting algorithm, called FloatBoost, is proposed to overcome the monotonicity problem of the sequential AdaBoost learning. AdaBoost [1,2] is a sequential forward search procedure using the greedy selection strategy. The premise offered by the sequential procedure can be broken-down when the monotonicity assumption, i.e. that when adding a new feature to the current set, the value of the performance criterion does not decrease, is violated. FloatBoost incorporates the idea of Floating Search [3] into AdaBoost to solve the non-monotonicity problem encountered in the sequential search of AdaBoost.
We then present a system which learns to detect multi-view faces using FloatBoost. The system uses a coarse-to-fine, simple-to-complex architecture called detector-pyramid. FloatBoost learns the component detectors in the pyramid and yields similar or higher classification accuracy than AdaBoost with a smaller number of weak classifiers. This work leads to the first real-time multi-view face detection system in the world. It runs at 200 ms per image of size 320×240 pixels on a Pentium-III CPU of 700 MHz. A live demo will be shown at the conference.
The work presented in this paper was carried out at Microsoft Research Asia.
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Li, S.Z., Zhu, L., Zhang, Z., Blake, A., Zhang, H., Shum, H. (2002). Statistical Learning of Multi-view Face Detection. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds) Computer Vision — ECCV 2002. ECCV 2002. Lecture Notes in Computer Science, vol 2353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47979-1_5
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DOI: https://doi.org/10.1007/3-540-47979-1_5
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