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.