(54.173.237.152) 您好!臺灣時間:2019/02/22 23:05
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
本論文永久網址: 
line
研究生:陳維荏
研究生(外文):Wei-Ren Chen
論文名稱:以流形學習法建立容貌模型以進行臉部特徵萃取
論文名稱(外文):Facial Feature Extraction by Manifold Learning of Appearance Model
指導教授:王元凱王元凱引用關係
指導教授(外文):Yuan-Kai Wang
學位類別:碩士
校院名稱:輔仁大學
系所名稱:電子工程學系
學門:工程學門
學類:電資工程學類
論文出版年:2008
畢業學年度:96
語文別:英文
論文頁數:48
外文關鍵詞:Manifold LearingActive Appearance ModelLocality Preserving Projections
相關次數:
  • 被引用被引用:1
  • 點閱點閱:1048
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:68
  • 收藏至我的研究室書目清單書目收藏:0
對人臉辨識或人臉表情辨識來說,正確的定位眼睛、鼻子、嘴巴是一個關鍵的過程。傳統的方法如Active Appearance Model (AAM) 使用了 Principal Component Analysis (PCA) 去降低容貌資料的維度,並在搜尋過程中求取容貌資料的最小誤差去得到人臉特徵。在本論文中,我們提出一個以流形為基礎的人臉特徵萃取方法,此流形學習法稱之為Locality Preserving Projection (LPP)。LPP以計算鄰近資料的關係將容貌資料投影成低維度的資料而非用變異數的方式,且可以保存容貌資料之局部結構與重要特性。本實驗資料從AR人臉資料庫中使用了870張影像,其中包含了光源與表情變化,並使用了PIE人臉資料庫中200張的影像,其中包含了各種的姿勢。實驗結果表示本論文所提出之方法能獲得比AAM更好的效果。
Extracting accurate positions of eyes, nose and mouth, is a crucial process for face recognition and facial expression recognition. Classical methods such as Active Appearance Model (AAM) use the principal component analysis to reduce the dimensionality of appearance data, and an iterative search to find facial features by minimizing an error criteria of the reduced appearance data. In this paper, we propose a facial feature extraction approach by manifold learning. The manifold learning method, locality preserving projection (LPP), projects appearance data into low-dimensional data by considering neighborhood relation but not principal directions of variances. The LPP can preserve local structure of appearance data, and remain most of the important characteristics of the appearance data. Experimental data includes 870 images from AR face database which includes variations of illumination and expression, and 200 images from PIE face database which includes different poses. Experimental results show that the proposed method has better performance than that of the AAM method.
Abstract (in Chinese) i
Abstract ii
Acknowledgement (in Chinese) iii
Contents iv
List of Tables v
List of Figures vi
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation 2
1.3 Review of Literature 3
1.4 The organization of this paper 5
Chapter 2 Active Appearance Models Algorithm 6
2.1 Appearance models 7
2.2 Building models 10
2.3 Iterative search 12
2.4 Appearance sampling by triangulation algorithm 14
Chapter 3 Locality Preserving Projections Algorithm and System Framework 19
3.1 Locality preserving projections 20
3.2 Our proposed approach 24
Chapter 4 Experimental Results 28
4.1 Database 28
4.2 Cross validation result 31
4.3 Different number of samplings 41
4.4 Different database for training and test 42
4.5 Effects of sampling region 43
4.6 Effects of face detection 44
4.7 Discussion 45
Chapter 5 Conclusions 46
References 47
[1]T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, “Active shape models - their training and application,” Computer Vision and Image Understanding, vol. 61 no. 1, pp. 38-59, Jan. 1995.
[2]T. F. Cootes, G. Edwards, and C. J. Taylor, “Active appearance models,” in Proc. Eur. Conf. Computer Vision, Freiberg, Germany, vol. 2, 1998, pp. 484-498.
[3]M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in Proc. IEEE International Conference on Computer Vision and Pattern Recognition, Maui, HI, Jun. 1991, pp. 586-591.
[4]X. Hou, S. Z. Li, H. Zhang, and Q. Cheng, “Direct appearance models,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, Dec. 2001, pp. 828-833.
[5]S. Z. Li, S. C. Yan, H. J. Zhang, and Q. S. Cheng, “Multi-view face alignment using direct appearance models,” in Proc. Conf. on Automatic Face and Gesture Recognition, Washington, DC, May 2002. pp. 324-329.
[6]S. Yan, X. Hou, S. Z. Li, H. Zhang, and Q. Cheng, “Face alignment using view-based direct appearance models,” International Journal of Imaging Systems and Technology, vol. 13, no. 1, pp. 106-112, 2003.
[7]A. U. Batur and M. H. Hayes, “Adaptive active appearance models” IEEE Trans. Image Processing, vol. 14, no. 11, pp. 1707-1721, 2005.
[8]S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science, vol. 290, Dec. 2000. pp. 2323-2326.
[9]L. K. Saul and S. T. Roweis, “Think globally, fit locally: unsupervised learning of low dimensional manifolds,” J. Machine Learning Research, vol. 4, pp. 119-155, Jun. 2003.
[10] J. B. Tenenbaum, V. D. Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science, vol. 290, no. 22, Dec. 2000, pp. 2319-2323.
[11] M. Belkin and P. Niyogi, “Laplacian eigenmaps and spectral techniques for embedding and clustering,” in Proc. 14th Conf. Neural Information Processing Systems, Cambridge, MA, 2002, pp. 585-591.
[12] D. Donoho and C. Grimes, “Hessian eigenmaps: locally linear embedding techniques for high dimensional data,” in Proc. Nat. Acad. of Sci., vol. 100, no. 10, 2003. pp. 5591–5596.
[13]Y. Bengio, J. F. Paiement, and P. Vincent. “Out-of-sample extensions for lle, isomap, mds, eigenmaps and spectral clustering,” in Proc. 16th Conf. Neural Information Processing Systems, Vancouver, Canada, 2003, pp. 177-184.
[14]X. He and P. Niyogi, “Locality preserving projections,” in Proc. 16th Conf. Neural Information Processing Systems, Vancouver, Canada, 2003, pp. 585-591.
[15]T. F. Cootes, D. J. Edwards, and S. J. Tylor, “Active appearance models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 6, pp. 681-685, Jun. 2001.
[16]T. F. Cootes and C. J. Taylor. (2004, March 8) Statistical models of appearance for computer vision. [Online]. Available: http://www.isbe.man.ac.uk/~bim/Models/app_models.pdf
[17]D. T. Lee and B. J. Schacter, “Two algorithms for constructing a Delaunay triangulation,” International Journal of Computer and Information Sciences, vol. 9 no. 3, pp. 219-242, 1980.
[18]P. Viola and M. J. Jones, “Robust real-time face detection,” International Journal of Computer Vision, vol. 57, no. 2, pp. 137-154, 2004.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔