| 摘要(英) |
Adaboost is the most widely used in face detection system. As reported in several literatures, face detection is usually carried out based on face image feature with a fixed integer weight through training. Whether the selected features from human face samples guarantee to be the best features or they can improve the potential correct rate while decreasing the false positive rate quickly is a topic worth investigating. Moreover, once the face detection techniques have been developed to a certain degree, the breaking of current bottlenecks toward a profound standard is still a long way to go.
In this thesis, we present a novel training method to improve Adaboost classifier for enhancing the detection performance. In our proposed method, it will reduce more false alarm than traditional Adaboost because the original features in Viola’s Adaboost used fixed integer weight features. The results show that the using of floating point weight will indeed select better features for detection. We abandon traditional training methods by using LDA and PCA to adjust each weight for obtaining the best feature. In addition to using LDA and PCA features, NDA feature is also integrated to decrease false alarm. In the training stage, we can also quickly decrease the false positive rate while achieving better detection results.
We then train the system architecture to make profound improvement for original floating point feature weight [9] in respect to speed. That is features are trained from the original Adaboost, then be fed to the second training process using LDA,PCA and NDA algorithms to adjust the feature weight combination as parallel processing for each selected feature. Finally, voting mechanism is adopted to select the last feature to achieve the best face detection rate.
Keyword : Face detection, AdaBoost, FLDA, PCA, NDA.
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| 參考文獻 |
[1] P. Viola and M. Jones, “ Rapid object detection using a boosted cascade of simple feature”, Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, Dec 2001.
[2] Henry Schneiderman, Takeo Kanade, “A statistical method for 3d object detection applied to faces and cars”, Proc of Computer Vision and Pattern Recognition (1):746–751, 2000.
[3] Edgar Osuna, Robert Freund, Federico Girosi, “Training support vector machines: an application to face detection”, In Computer Vision and Pattern Recognition 130–136, 2000.
[4] Christophe Garcia, Manolis Delakis, Convolutional face finder: “A neural architecture for fast and robust face detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence 26(11):1408–1423, 2004.
[5] Yongmin Li, Shaogang Gong, H. Liddell, “Support vector regression and classification based multi-view face detection and recognition”, IEEE International Conference on Automatic Face and Gesture Recognition, pp. 300–305, 2000.
[6] Lindsay I Smith, “A Tutorial on Principal Components Analysis”, 2002.
[7] Yusuke Nara, Jianming Yang, Yoshikazu Suematsu, “Face Recognition Using Improved Principal Component Analysis”, Proc. International Symposium on Micromechatronics and Human Science, pp.77–82, 2003.
[8] Hua Yu, Jie Yang “A Direct LDA Algorithm for High-Dimensional Data with Application to Face Recognition”, Pattern Recognition, vol. 34, no. 10, pp.2067–2070, 2001.
[9] Hakan Cevikalp, Marian Neamtu, Mitch Wilkes , Atalay Barkana, “Discriminative Common Vectors for Face Recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 1, January 2005.
[10] Paul Viola, Michael Jones, “Robust Real Time Object Detection,” IEEE ICCV Workshop Statistical and Computational Theories of Vision, July 2001.
[11] Liansheng ZHUANG, Ketan, TANG, Nenghai YU, “Fast Salient Object Detection Based on Segments,” International Conference on Measuring Technology and Mechatronics Automation, January 2009.
[12] Takeshi Mita, Toshimitsu Kaneko, Bjorn Stenger, Osamu Hori, “Discriminative Feature Co-Occurrence Selection for Object Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 7, JULY 2008.
[13] Sri-Kaushik Pavani, David Delgado, Alejandro F. Frangi, “Haar-like features with optimally weighted rectangles for rapid object dectection”, Pattern Recognition, May, 2009.
[14]http://debut.cis.nctu.edu.tw/~ching/Face/TheFaceProject%20on%20Boosting%20algorithm/Rapid%20Object%20Detection%20using%20a%20Boosted%20Cascade%20of.ppt
[15] http://www.cmlab.csie.ntu.edu.tw/~cyy/learning/tutorials/LDA.pdf
[16] http://www.cmlab.csie.ntu.edu.tw/~cyy/learning/tutorials/PCAMissingData.pdf
[17] K.Fukunaga, J.M.ManTock, “Nonparametric Discriminant Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-5, No.6, Novenber 1983.
[18] Peng Wang, Qiang Ji, “Multi-view face and eye detection using discriminant features”, Computer Vision and Image Understanding 105 (99-111), 2007.
[19] Yoav Freund, Robert E. Schapire, “A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting,” Journal of Computer and System Sciences, 55(l): 119-139, 1997.
[20] H.A. Rowley, S. Baluja, T. Kanade, “Neural network-based face detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20 (1) (1998) 23–38.
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