dynamic cascades for face detection 第三組 馮堃齊、莊以暘. 2009/01/072 outline...

Dynamic Cascades for Face Detection

第三組馮堃齊、莊以暘

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Outline

• Introduction

• Dynamic Cascade

• Boosting with a Bayesian Stump

• Experiments

• Conclusion

• Reference

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Introduction

• Adaboost cascade– First highly-accurate real-time face detector.

• Training rapid classifiers on data sets with large numbers of negative samples.– Yeilds low false alarm rate.

• Once a positive sample is misclassified, it cannot be corrected.

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Dynamic Cascade

• Training face detector using data set with massive numbers of positive and negative samples.

• Using only a small subset of training data, called “dynamic working set”, for boost training.

• Updating the dynamic working set when its distribution is less representative of the whole training data.

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Dynamic Cascade

• Rejection threshold– Trade-offs between speed and detection rate.

• False negative rate vt

– k: normalization factor.– α: free parameter to trade between detection

speed and accuracy.

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Learning From Multiple Feature Sets

1. Haar-like features.

2. Gabor wavelet features.

3. EOH (Edge Orientation Histogram) features.

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Dynamic Cascade Learning

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Dynamic Cascade Learning

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Dynamic Cascade Learning

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Boosting with a Bayesian Stump

• Extending the naive decision stump to a single-node multi-way split decision tree method.

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Bayesian Error

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Bayesian Stump

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Bayesian Stump

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Experiments

• Positive set: 531141 samples. (including shift, scale, and rotation)

• Validation set: 40000 samples.

• Negative set: 10 billion samples.

• Sample size: 24 x 24

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Experiments

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The Importance of Using Large Training Data Sets

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The Effects of Using Different Weak Classifiers

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The Effects of Using Different Alpha Parameters

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The Effects of Using Multiple Feature Sets

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Performance Comparisons on Multiple Data Sets

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Conclusion

• Introducing a novel algorithm called dynamic cascade for robust face detection.

• Contributions:– Using a dynamic working set for bootstrapping

positive samples.– New weak classifier called Bayesian stump.– A novel strategy for learning from multiple

feature sets.

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Reference

• S. C. Brubacker, M. D. Mullin, and J. M. Rehg. Towards optimal training of cascade classifiers. In Proc. of European Conference on Computer Vision, 2006.

• H. Luo. Optimization design of cascaded classifiers. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 2005.

• P.Viola andM. Jones. Rapid object detection using a boosted cascade of simple features. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, volume 1, pages 511–518, 2001.