政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/37112

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/37112

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政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/37112

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题名:	基於方向性邊緣特徵之即時物件偵測與追蹤 Real-Time Object Detection and Tracking using Directional Edge Maps
作者:	王財得 Wang, Tsai-Te
贡献者:	廖文宏 Liao, Wen-Hung 王財得 Wang, Tsai-Te
关键词:	物件偵測方向性邊緣特徵人臉偵測表情識別 Object Detection Directional Edge Maps Face Detection Facial Expression Recognition AdaBoost
日期:	2007
上传时间:	2009-09-19 12:10:47 (UTC+8)
摘要:	在電腦視覺的研究之中，有關物件的偵測與追蹤應用在速度及可靠性上的追求一直是相當具有挑戰性的問題，而現階段發展以視覺為基礎互動式的應用，所使用到技術諸如:類神經網路、SVM及貝氏網路等。本論文中我們持續深入此領域，並提出及發展一個方向性邊緣特徵集(DEM)與修正後的AdaBoost訓練演算法相互結合，期能有效提高物件偵測與識別的速度及準確性，在實際驗證中，我們將之應用於多種角度之人臉偵測，以及臉部表情識別等兩個主要問題之上；在人臉偵測的應用中，我們使用CMU的臉部資料庫並與Viola & Jones方法進行分析比較，在準確率上，我們的方法擁有79% 的recall及90% 的precision，而Viola & ones的方法則分別為81%及77%；在運算速度上，同樣處理512x384的影像，相較於Viola & Jones需時132ms，我們提出的方法則有較佳的82ms。此外，於表情識別的應用中，我們結合運用Component-based及Action-unit model 兩種方法。前者的優勢在於提供臉部細節特徵的定位及追蹤變化，後者主要功用則為進行情緒表情的分類。我們對於四種不同情緒表情的辨識準確度如下：高興(83.6%)、傷心(72.7%)、驚訝(80%) 、生氣(78.1%)。在實驗中，可以發現生氣及傷心兩種情緒較難區分，而高興與驚訝則較易識別。 Rapid and robust detection and tracking of objects is a challenging problem in computer vision research. Techniques such as artificial neural networks, support vector machine and Bayesian networks have been developed to enable interactive vision-based applications. In this thesis, we tackle this issue by devising a novel feature descriptor named directional edge maps (DEM). When combined with a modified AdaBoost training algorithm, the proposed descriptor can produce effective results in many object detection and recognition tasks. We have applied the newly developed method to two important object recognition problems, namely, face detection and facial expression recognition. The DEM-based methodology conceived in this thesis is capable of detecting faces of multiple views. To test the efficacy of our face detection mechanism, we have performed a comparative analysis with the Viola and Jones algorithm using Carnegie Mellon University face database. The recall and precision using our approach is 79% and 90%, respectively, compared to 81% and 77% using Viola and Jones algorithm. Our algorithm is also more efficient, requiring only 82 ms (compared to 132 ms by Viola and Jones) for processing a 512x384 image. To achieve robust facial expression recognition, we have combined component-based methods and action-unit model-based approaches. The component-based method is mainly utilized to locate important facial features and track their deformations. Action-unit model-based approach is then employed to carry out expression recognition. The accuracy of classifying different emotion type is as follows: happiness 83.6%, sadness 72.7%, surprise 80%, and anger 78.1%. It turns out that anger and sadness are more difficult to distinguish, whereas happiness and surprise expression have higher recognition rates.
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Friesen, “Constants Across Cultures in the Face and Emotion”, Journal of personality and Social Psychology, pp.124-129, 1971. [15] G. Littlewort, M. Bartlett, I. Fasel, J. Susskind and Javier Movellan. ”Dynamics of Facial Expression Extracted Automatically from Video”, Proceeding of Computer Vision and Pattern Recognition, pp.80, 2004. [16] S.C. Tai, K.C. Chung. ”Automatic Facial Expression Recognition System Using Neural Networks”, Proceedings of TENCON, pp.1~4, 2007. [17] M. P. Loh, Y. Wong and C. Wong, “Facial Expression Recognition for E-learning Systems using Gabor Wavelet Neural Network”, Proceedings of International Conference on Advanced Learning Technologies, pp.523~525, 2006. [18] J. Y. Chang and J. Chen, “Automated Facial Expression Recognition System Using Neural Networks”, Journal of the Chinese Institute of Engineers, 2001. [19] F. Tang and B. Deng, “Facial Expression Recognition using AAM and Local Facial Features”, Proceedings of Interdisciplinary Center for Neural Computation, pp.632~635, 2007. [20] W. Liu and Z. Wang, “Facial Expression Recognition Based on Fusion of Multiple Gabor Features“, Proceeding of Computer Vision and Pattern Recognition, Vol.3, pp.536~539, 2006. [21] C. Shan, S. Haogang, G. McOwan and P. W, “Recognizing Facial Expressions at Low Resolution”, Proceedings of Advanced Video and Signal Based Surveillance, pp.330~335, 2005. [22] Q. Xu, P. Zhang, W. Pei, L. Yang and Z. He, “An Automatic Facial Expression Recognition Approach Based on Confusion-Crossed Support Vector Machine Tree”, Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Vol.1, pp.I-625~I-628, 2007. [23] I. Kotsia, N. Nikolaidis and I. Pitas, ”Facial Expression Recognition in Videos Using a Novel Multi-Class Support Vector Machines Variant”. Proceedings of International Conference on Acoustics, Speech, and Signal Processing, Vol.2, pp.II-585~II-588, 2007. [24] J. Tang, Z. Youwei, “Facial Expression Recognition Based on Motion Information Between Frames”, Proceedings of 8th International Conference on Signal Processing, vol.3, pp.16-20, 2006. [25] A. Kanaujia, D. Metaxas, “Recognizing Facial Expressions by Tracking Feature Shapes”, Proceedings of Computer Vision and Pattern Recognition, Vol.2, pp.33-38, 2006. [26] S. Jung, D. H. Kim, K. Ho and M. J. Chung, “Efficient Rectangle Feature Extraction for Real-time Facial Expression Recognition based on AdaBoost”, Proceedings of Intelligent Robots and Systems, pp.1941-1946, Aug. 2005. [27] H. Deng, J. Zhu, M. R. Lyu and I. King, “Two-stage Multi-class AdaBoost for Facial Expression Recognition”, Proceedings of International Joint Conference on Neural Networks, pp. 3005-3010, 2007. [28] P. S. Aleksic and A. K. Katsaggelos, “Automatic Facial Expression Recognition Using Facial Animation Parameters and Multistream HMMs”, Transactions on Information Forensics and Security, Vol.1, Issue:1, pp.3~11, 2006. [29] Q. X. Xu and J. Wei, “Application of Wavelet Energy Feature in Facial Expression Recognition”, Proceedings of IEEE International Workshop on Anti-counterfeiting Security, Identification, pp.169~174, 2007. [30] K. S. Cho, Y. Kim and Y.ang-Bok Lee, “Real-time Expression recognition System using Active Appearance Model and EFM”, Proceedings of Computational Intelligence and Security, Vol.1, pp. 747~750, 2006 [31] D. Torre, F. Campoy, J. Ambadar, Z. Cohn and F. Jeffrey, “Temporal Segmentation of Facial Behavior”, Proceedings of International Conference on Computer Vision, pp.1~8, 2007. [32] X. Feng, B. Lv and Z. Li, “Automatic Facial Expression Recognition Using Both Local and Global Information”, Proceedings of Chinese Control Conference, pp.1878~1881, 2006. [33] G. Zhou, Y. Zhan and J. Zhang, “Facial Expression Recognition Based on Selective Feature Extraction”, Proceedings of Intelligent Systems Design and Applications, Vol.2, pp.412~417, 2006. [34] F. Dornaika, F. Davoine, “Facial Expression Recognition using Auto-regressive Models”, Proceeding of Computer Vision and Pattern Recognition, Vol.2, pp.520-523, 2006. [35] M. A. Turk, A. P. Pentland, “Face Recognition Using Eigenfaces” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586-591, 1991. [36] T. F. Cootes, G. J. Edwards, and C. J. Taylor. ”Active Appearance Models” IEEE　Transactions on Pattern Analysis and Machine Intelligence, Vol.23, Issue:6, pp.681-685, 2001. [37] T. F. Cootes, C. J. Taylor, D. H. Cooper and J. Graham, ”Training models of shape from sets of examples”, Proceedings of British Machine Vision Conference, pp. 266–275, 1992. [38] S. Baker, R. Gross, and I. Matthews, “Lucas-Kanade 20 Years On: A Unifying Framework: Part 3” tech. report Carnegie Mellon University, 2003.
描述:	碩士國立政治大學資訊科學學系 95971003 96
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0095971003
数据类型:	thesis
显示于类别:	[資訊科學系] 學位論文

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