政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/70998

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/70998

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政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/70998

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/70998

題名:	基於 RGBD 影音串流之肢體表情語言表現評估 Estimation and Evaluation of Body Language Using RGBD Data
作者:	吳怡潔 Wu, Yi Chieh
貢獻者:	廖文宏 Liao, Wen Hung 吳怡潔 Wu, Yi Chieh
關鍵詞:	肢體語言 RGBD Kinect 感測器表現評估聲音處理模式分類 Body language RGBD Kinect sensor performance evaluation audio processing pattern classification
日期:	2013
上傳時間:	2014-11-03 10:11:57 (UTC+8)
摘要:	本論文基於具備捕捉影像深度的RGBD影音串流裝置－Kinect感測器，在簡報場域中，作為擷取簡報者肢體動作、表情、以及語言表現模式的設備。首先我們提出在特定時段內的表現模式，可以經由大眾的評估，而具有喜歡／不喜歡的性質，我們將其分別命名為Period of Like(POL)以及Period of Dislike(POD)。論文中並以三種Kinect SDK所提供的影像特徵：動畫單元、骨架關節點、以及3D臉部頂點，輔以35位評估者所提供之評估資料，以POD/POL取出的特徵模式，分析是否具有一致性，以及是否可用於未來預測。最後將研究結果開發應用於原型程式，期許這樣的預測系統，能夠為在簡報中表現不佳而困擾的人們，提點其優劣之處，以作為後續改善之依據。 In this thesis, we capture body movements, facial expressions, and voice data of subjects in the presentation scenario using RGBD-capable Kinect sensor. The acquired videos were accessed by a group of reviewers to indicate their preferences/aversions to the presentation style. We denote the two classes of ruling as Period of Like (POL) and Period of Dislike (POD), respectively. We then employ three types of image features, namely, animation units (AU), skeletal joints, and 3D face vertices to analyze the consistency of the evaluation result, as well as the ability to classify unseen footage based on the training data supplied by 35 evaluators. Finally, we develop a prototype program to help users to identify their strength/weakness during their presentation so that they can improve their skills accordingly.
參考文獻:	[1] Rachael E. Jack, “Facial expressions of emotion are not culturally universal”, PNAS online, 2012. [2] Alex Pentland, ” Honest Signals : How They Shape Our World”, The MIT Press, August.2008. [3] Oxford Dictionaries, http://www.oxforddictionaries.com/definition/english/body-language. [4] Wikipedia contributors, "Kinect," Wikipedia, The Free Encyclopedia, http://en.wikipedia.org/w/index.php?title=Kinect&oldid=612754262 (accessed June 29, 2014). [5] MSDN, ”Face Tracking", http://msdn.microsoft.com/en-us/library/jj130970.aspx. [6] MSDN, ”Tracking Users with Kinect Skeletal Tracking", http://msdn.microsoft.com/en-us/library/jj131025.aspx. [7] Microsoft, “Pre-order the Kinect for Windows v2 sensor”, http://www.microsoft.com/en-us/kinectforwindows/Purchase/developer-sku.aspx [8] M. E. Hoque, M. Courgeon, B. Mutlu, J-C. Martin, R. W. Picard, “MACH: My Automated Conversation coacH “, In the 15th International Conference on Ubiquitous Computing (Ubicomp), September 2013. [9] S. Feese, B. Arnrich, G. Tröster, B. Meyer, K. Jonas, “Automatic Clustering of Conversational Patterns from Speech and Motion Data”, Measuring Behavior 2012. [10] Nick Morgan, “7 Surprising Truths about Body Language”, http://www.forbes.com/sites/nickmorgan/2012/10/25/7-surprising-truths-about-body-language/. [11] Alex Pentland, ” Honest Signals : How They Shape Our World”, The MIT Press, p.3-4, August.2008. [12] Marco Pasch, Monica Landoni, “Building Corpora of Bodily Expressions of Affect”, Measuring Behavior 2012. [13] Xsens MVN suit, http://www.xsens.com/products/xsens-mvn/. [14] Wouter van Teijlingen, Egon L. van den Broek, Reinier Könemann, John G.M. Schavemaker, “Towards Sensing Behavior Using the Kinect”, Measuring Behavior 2012. [15] MSDN, “Using the Kinect as an Audio Device”, http://msdn.microsoft.com/en-us/library/jj883682.aspx. [16] Posner MI, “Timing the Brain: Mental Chronometry as a Tool in Neuroscience”, PLoS Biol 3(2): e51. doi:10.1371/journal.pbio.0030051, 2005, http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030051. [17] Ken Goldberg, Siamak Faridani, Ron Alterovitz, “A New Derivation and Dataset for Fitts` Law of Human Motion”, Technical Report No. UCB/EECS-2013-171 , October 22, 2013, http://www.tele-actor.net/fitts-dataset/. [18] FFmpeg, https://www.ffmpeg.org/. [19] w3schools, “HTML <video> Tag”, http://www.w3schools.com/tags/tag_video.asp. [20] ETSI, 2002, http://www.etsi.org/, http://www.etsi.org/deliver/etsi_es/202000_202099/202050/01.01.05_60/. [21] ETSI, “Speech Processing, Transmission and Quality Aspects (STQ); Distributed speech recognition; Advanced front-end feature extraction algorithm; Compression algorithms”, 2002, http://www.etsi.org/deliver/etsi_es/202000_202099/202050/01.01.05_60/es_202050v010105p.pdf. [22] A. Acero, C. Crespo, C. de la Torre, J. C. Torrecilla, “Robust HMM-based endpoint detector”, Third European Conference on Speech Communication and Technology, EUROSPEECH 1993, Berlin, Germany, September 22-25, 1993. [23] Brian MacWhinney, “The CHILDES Project, Tools for Analyzing Talk – Electronic Edition, Part 2: The CLAN Programs”, http://childes.psy.cmu.edu/manuals/CLAN.pdf. [24] Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. [25] Heroku, https://www.heroku.com/. [26] Amazon S3, https://aws.amazon.com/documentation/s3/. [27] w3schools, “HTML Event Attributes”, http://www.w3schools.com/tags/ref_eventattributes.asp. [28] w3schools, “HTML DOM Events”, http://www.w3schools.com/jsref/dom_obj_event.asp. [29] R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/. [30] Giorgino T (2009). "Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package." _Journal of Statistical Software_,31(7), pp. 1-24. <URL: http://www.jstatsoft.org/v31/i07/>. [31] Tormene, P.; Giorgino, T.; Quaglini, S. & Stefanelli, M. Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation. Artif Intell Med, 2009, 45, 11-34. [32] MSDN, “NUI_SKELETON_DATA Structure”, http://msdn.microsoft.com/en-us/library/nuisensor.nui_skeleton_data.aspx. [33] MSDN, “Face Tracking Visualization C++ Sample”, http://msdn.microsoft.com/en-us/library/jj131045.aspx.
描述:	碩士國立政治大學資訊科學學系 101971004 102
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0101971004
資料類型:	thesis
顯示於類別:	[資訊科學系] 學位論文

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