Dynamic Cascades for Face Detection 第三組 馮堃齊、莊以暘

2009/01/072 Outline Introduction Dynamic Cascade Boosting with a Bayesian Stump Experiments Conclusion Reference

2009/01/073 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.

2009/01/074 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.

2009/01/075 Dynamic Cascade Rejection threshold –Trade-offs between speed and detection rate. False negative rate v t –k: normalization factor. –α: free parameter to trade between detection speed and accuracy.

2009/01/076 Learning From Multiple Feature Sets 1.Haar-like features. 2.Gabor wavelet features. 3.EOH (Edge Orientation Histogram) features.

2009/01/077 Dynamic Cascade Learning

2009/01/078 Dynamic Cascade Learning

2009/01/079 Dynamic Cascade Learning

2009/01/0710 Boosting with a Bayesian Stump Extending the naive decision stump to a single-node multi-way split decision tree method.

2009/01/0711 Bayesian Error

2009/01/0712 Bayesian Stump

2009/01/0713 Bayesian Stump

2009/01/0714 Experiments Positive set: samples. (including shift, scale, and rotation) Validation set: samples. Negative set: 10 billion samples. Sample size: 24 x 24

2009/01/0715 Experiments

2009/01/0716 The Importance of Using Large Training Data Sets

2009/01/0717 The Effects of Using Different Weak Classifiers

2009/01/0718 The Effects of Using Different Alpha Parameters

2009/01/0719 The Effects of Using Multiple Feature Sets

2009/01/0720 Performance Comparisons on Multiple Data Sets

2009/01/0721 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.

2009/01/0722 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, H. Luo. Optimization design of cascaded classifiers. In Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 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.