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研究生: 黃律嘉
Liu-Jia Huang
論文名稱: 以主成份分析為基礎之嬰兒表情辨識系統
An Infant Facial Expression Recognition System Based on Principal Component Analysis
指導教授: 方瓊瑤
Fang, Chiung-Yao
學位類別: 碩士
Master
系所名稱: 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年: 2011
畢業學年度: 99
語文別: 中文
論文頁數: 70
論文種類: 學術論文
相關次數: 點閱:136下載:13
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  • 本論文提出一套具有辨識嬰兒表情功能的視覺式監控系統。因為年幼的嬰兒還不會用言語來表達自身的需求,我們只能透過嬰兒臉上的表情來了解嬰兒是否需要幫助。此外全天候照顧嬰兒需要花費許多精力,本研究擬建立智慧型表情辨識輔助系統,藉由判別嬰兒的表情,隨時告知照顧者嬰兒的現況,系統如果偵測到嬰兒哭泣或吐奶時,能及時發出警告提醒照顧者檢視嬰兒的情況,以求降低嬰兒意外的發生。
    本研究將攝影機架設在嬰兒床前方,拍攝嬰兒的臉部影像,輸入至系統作分析。其中系統可分兩個子系統:嬰兒臉部偵測以及嬰兒表情辨識。嬰兒臉部偵測是利用Locus model定義的膚色範圍找出影像中的膚色像素,並利用connected component的技術找出最大的膚色區塊,經由區塊分析可找到嬰兒臉部區域,完成嬰兒臉部定位後。即可擷取出嬰兒臉部區域影像。
    在臉部特徵擷取部分,本研究採用主成份分析(principal component analysis)的方法,取出具有代表性的臉部特徵,並將訓練影像的特徵向量與投影矩陣存入資料庫中。之後只要將臉部表情的連續影像輸入至系統,並和資料庫中的投影矩陣相乘就能得到單張影像的特徵向量,與資料庫中的各表情影像做比對後,可得出該影像的表情類別。
    因為單張表情辨識的正確率無法提高,我們考慮連續影像的表情發生機率,直到有某種表情類別的發生機率超過系統預設的臨界值,系統才將辨識結果輸出,由實驗結果證明本系統是穩定且有效率的。

    This thesis presents an automatic infant facial expression recognition system. Since infants are too young to say what they need, we can only guess the infants’ needs by their facial expressions. Moreover, to take care of the infants from day and night is a hard work. An infant facial expression recognition system can help baby sitters to monitor the infants to decrease their load and to avoid the infant accidents.
    We set up the camera on the infant’s crib and obtain the infant’s face images to input into the system. The system can be divided into two main stages: infant face localization and facial expression recognition. At the infant face localization stage, the system applies the locus model to detect the skin color pixels from the input images. After that, the system uses the connect component technology to find the biggest skin color region which is regarded as the infant’s face.
    At the facial expression recognition stage, the system analyzes and classifies infant’s face images according to their facial expressions. The system classifies the facial expressions based on the feature vectors extracted by the principal component analysis method (PCA). Once a testing image input into the system, the feature vector is extracted by PCA and is classified by the nearest Euclidean distance method.
    Since the error rate of individual image cannot be reduced, the system considers the evidence supported by successive images and outputs the final facial expression if the evidence is enough. The experimental results show that the proposed system is robust and efficient.

    目錄 iv 附圖目錄 v 附表目錄 vii 第一章 緒論 1-1 1.1研究背景與目的 1-1 1.2 文獻探討 1-3 1.2.1膚色偵測之困難 1-3 1.2.2 色彩空間 1-4 1.2.3膚色模型和判別膚色的分類法 1-6 1.2.4 特徵擷取 1-10 1.3論文架構 1-11 第二章 嬰兒表情辨識系統與前處理 2-1 2.1 系統目的 2-1 2.2系統流程 2-2 2.3.1膚色擷取 2-4 2.3.2 區塊分析 2-7 2.3.3 嬰兒臉部定位流程圖 2-8 2.3.4影像降維 2-9 第三章 PCA人臉辨識與表情發生機率調整 3-1 3.1 主成份分析(principal component analysis, PCA) 3-1 3.1.1 特徵臉(eigenface) 3-3 3.1.2 單張影像比對 3-5 3.2 連續影像表情機率調整 3-6 第四章 實驗結果與討論 4-1 4.1 實驗環境 4-1 4.2 膚色偵測 4-1 4.3 嬰兒臉部定位 4-4 4.4 PCA人臉辨識 4-13 4.5發生機率調整 4-23 第五章 結論與未來工作 5-1 5.1 結論 5-1 5.2未來工作 5-2 參考文獻 A

    [Kak07] P. Kakumanu, S. Makrogiannis, and N.Bourbakis, “A survey of skin-color modeling and detection methods,” The Journal of the Pattern Recognition Society, pp.1106-1122, 2007.
    [Yan98] J. Yang, W. Lu, and A. Waibel, “Skin-color modeling and adaptation,” Proceedings of the Third Asian Conference on Computer Vision (ACCV 1998), Hong Kong, China, 1998.
    [Cha99] D. Chai and K. N. Ngan, “Face segmentation using skin-color map in videophone application,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 9, no. 4, pp.551-564, 1999.
    [Hsu02] R. L. Hsu, M. Abdel-Mottaleb, and A. K. Jain, “Face detection in color images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, no. 5, pp.696-706, 2002.
    [Zha09] P. Zhang, “A Video-based face detection and recognition system using cascade face verification modules,” Proceedings of the Applied Imagery Pattern Recognition Workshop (AIPR 2008), Washington, DC., 2008.
    [Sor02] M. Soriano, B. Martinkauppi, and S. Huovinen, “Skin detection in video under changing illumination conditions,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2002), Barcelona, Spain, Vol.1, pp.839-842, 2002.
    [Phu05] S. L. Phung, A. Bouzerdoum, and D. Chai, “Skin segmentation using color pixel classification: analysis and comparison,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, no. 1, pp.148-154, 2005.
    [Rav09] K. S. Ravichandran and B. Ananthi, “Color skin segmentation using k-Means clustering,” International Journal of Computational and Applied Mathematics, Vol. 4, no. 2, pp. 153-157, 2009.
    [Esa07] N. Esau, E. Wetzel, L. Kleinjohann, and B. Kleinjohann, “Real-time facial expression recognition using a fuzzy emotion model,” Proceedings of the IEEE International Conference on Fuzzy Systems, London, pp.1-6, 2007.
    [Tom94] C. Tomasi and J. Shi, “Good features to track,” Proceedings of the 9th IEEE Computer Society Conference on Computer Vision and Pattern Recognition, California, pp. 593-600, 1994.
    [Pal05] P. Pal, A. N. Iyer, and R. E. Yantorno, “Emotion detection from infant facial expression and cries,” Proceedings of the 2006 IEEE International Conference on Document Analysis and Recognition, Toulouse, France, Vol. 2, pp. 721-724, 2005.
    [Ter00] J. C. Terrillon, M. N. Shirazi, H. Fukamachi, and S. Akamatsu, “Comparative performance of different skin chrominance models and chrominance spaces for the automatic detection of human faces in color images,” Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition, Grenoble, France, pp. 54-61, 2000.
    [Ton10] Y. Tong, J. Chen, and Q. Ji, “A unified probabilistic framework for spontaneous facial action modeling and understanding,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, no. 2, pp. 258-273, 2010.
    [Udd09] M. Z. Uddin, J. J. Lee, and T. S. Kim, “An enhanced independent component-based human facial expression recognition from video,” IEEE Transactions on Consumer Electronics, Vol. 55, no. 4, pp. 2216-2224, 2009.
    [Son10] M. Song and D. Tao, “Image ratio features for facial expression recognition application,” IEEE Transactions on Systems, man, and Cybernetics, China, Vol. 40, no. 3, pp. 779-788, 2010.
    [Ekm78] P. Ekman and W. V. Friesen, “The facial action coding system: a technique for the measurement of facial movement,” San Francisco: Consulting Psychologists Press, 1978.
    [Sor03] M. Soriano, J. B. MartinKauppi, S. Huovinen, and M. Laaksonen, “Adaptive skin color modeling using the skin locus for selecting training pixels,” Pattern Recognition, Vol. 36, no. 3, pp. 681-690, 2003.
    [Oli97] N. Oliver, A. P. Pentland, and F. Berard, “LAFTER: lip and face real time tracker,” Computer Vision and Pattern Recognition, Vol. 33, pp. 123-129, 1997.
    [Wu99] H. Wu, Q. Chen, and M. Yachida, “Face detection from color images using a fuzzy pattern matching method,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. no. 6, pp. 557-563, 1999.
    [Che97] C. Chen and S. P. Chiang, “Detection of human faces in colour imges,” Proceedings of IEEE conference Vision Images Signal Process, Taichung, Vol. 144, pp. 384-388, 1997.
    [Thu02] Q. H. Thu, M. Meguro, and M. Kaneko, “Skin color extraction in images with complex background and varying illumination,” Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision, Japan, pp. 280-285, 2002.
    [Ter98] J. C. Terillon, M. David, and S. Akamatsu, “Detection of human faces in complex scene images by use of a skin color model and of invariant Fourier-Mellin moments,” Proceedings of the Fourteenth IEEE International Conference, Kyoto, Vol. 2, pp. 1350-1355, 1998.
    [Cha00] D. Chai and A. Bouzerdoum, “A Bayesian approach to skin color classification in YCbCr color space,” Proceedings of TENCON 2000, WA, Vol. 2, pp. 421-424, 2000.
    [Alp04] E. Alpaydin, “Introduction to Machine Learning,” MIT press, London, 2004.
    [Tur91] M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, Vol. 3, no. 1, pp.71-86, 1991.
    [Smi02] L. I. Smith, “A tutorial on Principal Components Analysis,” http://www.cs.otago.ac.nz/cosc453/student_tutorials/principal_components.pdf, February 26, 2002.
    [1] 內政部社會司,育齡婦女總生育率
    http://sowf.moi.gov.tw/stat/year/y02-04.xls
    [2] 行政院衛生署,國民健康局,出生通報統計資料http://www.bhp.doh.gov.tw/BHPnet/Portal/Them.aspx?No=200712250049
    [3] 台北市政府衛生局http://www.health.gov.tw/Default.aspx?tabid=626
    [陳10] 陳秋利,「自動膚色範圍界定之嬰兒臉部偵測及表情辨識系統」,國立師範大學研究所碩士論文,2010。
    [徐06] 徐士中,「任意背景下的人臉偵測」,國立台灣科技大學研究所碩士論文,2006。
    [邱06] 邱柏智,葉榮木,蔡俊明,「基於主成份分析與灰關聯度分析之人臉辨識」,2006第一屆電資科技應用與發展學術研討會,萬能科技大學,2006。
    [黃04] 黃泰祥,「具備人臉追蹤與辨識功能的一個智慧型數位監視系統」,私立中原大學碩士論文,2004。
    [李09] 李建興,林應璞,游凱倫,「訓練樣本數於人臉辨識之分析」,國立成功大學系統及船舶機電工程學系碩士論文,2009。
    [葉06] 葉冠麟,「Principal Component Analysis with Missing Data」,June 16,2006。http://www.cmlab.csie.ntu.edu.tw/~cyy/learning/tutorials/PCAMissingData.pdf
    [井上08] 井上誠喜,八木伸行,林 正樹,中須英輔,三谷公二,奧井誠人,「C語言數位影像處理」,全華圖書股份有限公司,2008。

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