研究生: |
張偉杰 |
---|---|
論文名稱: |
非使用者配合環境下之人臉辨識研究 Face Recognition in Non-Cooperative User Environment |
指導教授: | 李忠謀 |
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: | 2010 |
畢業學年度: | 98 |
語文別: | 中文 |
論文頁數: | 53 |
中文關鍵詞: | 人臉辨識 、使用者配合環境 、非使用者配合環境 |
英文關鍵詞: | Face Recognition, Cooperative User Environment, Non-Cooperative User Environment |
論文種類: | 學術論文 |
相關次數: | 點閱:97 下載:7 |
分享至: |
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
人臉辨識是近年來很熱門的研究,希望透過人臉辨識來達到身份的識別,而省去以往在不同的服務及場合下需要配戴很多張不同的證件。為此發展出來之人臉辨識,我們稱它為「使用者配合環境」,是在想要索取服務的情況下,進而配合攝影機來執行人臉辨識;反之在監視系統中,有人自然的從走廊經過,我們想透過走廊內的攝影機知道他是誰,在過程中該人並未配合攝影機來執行人臉辨識,所以像這類的情況我們稱它為「非使用者配合環境」,在本論文中將討論兩者之差異。文中我們透過在階梯教室裡不經由學生配合的情況下來執行人臉辨識,藉此來討論人臉辨識如何實現在非使用者配合環境下。
The researches of face recognition have been more and more popular in recent years. In different services and occasions, we need a lot of certificates to provide identity. By using the face recognition, we could save the paperwork. Therefore, the face recognition is developed by the above-mentioned that we call "Cooperative User Environment". It's a service which coordinates with the camera to implement the face recognition. On the other hand, we would like to know the person who passes through the corridor in the surveillance system. In this process, the person doesn't coordinate with the camera to implement the face recognition that we call "Non-Cooperative User Environment". In this paper, we will discuss the differences between cooperative user environment and non-cooperative user environment. We use the face recognition to make a roll call in the ladder classroom without the students' cooperation. In the experiments, we could know how the face recognition is implemented in the non-cooperative user environment.
參考文獻
[1] A.F. Abate, M. Nappi, D. Riccio, and G. Sabatino, “2D and 3D face recognition: A survey,” Pattern Recognition Letters, vol. 28, no. 14, pp. 1885-1906, Jan. 2007.
[2] Y. Adini, Y. Moses, and S. Ullman, “Face Recognition: The Problem of Compensation for Changes in Illumination Direction,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 721-732, Jul. 1997.
[3] M.S. Bartlett, H.M. Lades, and T.J. Sejnowski, “Independent component representation for face recognition,” In Proceedings of the SPIE Symposium on Electronic Imaging: Science and Technology; Conference on Human Vision and Electronic Imaging II, Netherlands, pp. 528-539, Jan. 1998.
[4] P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, Jul. 1997.
[5] S. Chen, H. Zhao, M. Kong, and B.Luo, “2D-LPP:A two-dimensional extension of locality preserving projections,” Neurocomputing, vol. 70, no. 4-6, pp. 912-921, Jan. 2007.
[6] B.A. Draper, K Baek, M.S. Bartlett, and J.R. Beveridge, “Recognizing Faces with PCA and ICA,” Computer Vision and Image Understanding, vol. 91, no. 1-2, pp. 115-137, Feb. 2003.
[7] M. Dubuisson and A.K. Jain, “A modified Hausdorff distance for object Matching,” Proc. 12th Int. Conf. on Pattern Recognition (ICPR), Jerusalem, Israel, pp. 566-568, Oct. 1994.
[8] Y. Gao, and M.K.H. Leung, “Face Recognition Using Line Edge Map,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 6, pp. 764-779, Jun. 2002.
[9] Y. Gao, and M.K.H. Leung, “Human Face Recognition Using Line Edge Maps,” Proc. IEEE Second Workshop Automatic Identification Advanced Technologies, New Jersey, USA, pp. 173-176, Oct. 1999.
[10] A. Hadid, “The Local Binary Pattern Approach and its Applications to Face Analysis,” Image Processing Theory, Tools and Applications, Sousse, Tunisia, pp. 1-9, Nov. 2008.
[11] X. He, and P. Niyogi, “Locality Preserving Projections,” Conference on Advances in Neural Information Processing System, Vancouver, Whistler, Canada, Dec. 2003.
[12] T. Heseltine, N. Pears, J. Austin, and Z. Chen, “Face recognition: A comparison of appearance-based approaches,” Digital Image Computing: Techniques and Applications, Sydney, NSW, Australia, pp.59-68, Dec. 2003.
[13] D.P. Huttenlocher, G.A. Klanderman, and W.J. Rucklidge, “Comparing images using the Hausdorff distance,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 850-863, Sep. 1993.
[14] M.K.H. Leung and Y.H. Yang, “Dynamic Two-Strip Algorithm in Curve Fitting,” Pattern Recognition, vol. 23, no. 1-2, pp. 69-79, Jan. 1990.
[15] S.Z. Li, R. Chu, S. Liao, and L. Zhang, “Illumination invariant face recognition using near-infrared images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 4, pp. 627-639, Apr. 2007.
[16] C. Liu, and H. Wechsler, “A shape- and texture-based enhanced fisher classifier for face recognition,” IEEE Trans. Image Processing, vol. 10, no. 4, pp. 598-608, Apr. 2001.
[17] A.M. Martinez, and A.C. Kak, “PCA versus LDA,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228-233, Feb. 2001.
[18] B. Miller, “Vital Signs of Identity,” IEEE Spectrum, pp. 22-30, Feb. 1994.
[19] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification Width Local Binary Patterns,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971-987, Jul. 2002.
[20] B. Takacs, “Comparing Face Images Using the Modified Hausdorff Distance,” Pattern Recognition, vol. 31, no. 12, pp. 1873-1881, Dec. 1998.
[21] A.S. Tolba, A.H. El-Baz, and A.A. El-Harby, “Face Recognition: A Literature Review,” International Journal of Signal Processing, vol. 2, no. 2, pp. 88-103, 2006.
[22] M. Turk, and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 72-86, 1991.
[23] P. Viola, and M. Jones, “Rapid object detection using a boosted cascade of simple features,” In IEEE Conference on Computer Vision and Pattern Recognition, Kauai Marriott, Hawaii, pp. 511-518, Dec. 2001.
[24] J. Yang, J. Yang, A.F. Frangi, and D. Zhang, “Uncorrelated projection discriminant analysis and its application to face image feature extraction,” International Journal of Pattern Recognition and Artificial Intelligence, vol. 17, no. 8, pp. 1325-1347, 2003.
[25] J. Yang, and D. Zhang, “Two-dimensional PCA: a new approach to appearance-based face representation and recognition,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 26, no. 1, pp. 131-137, Jan. 2004.
[26] J. Yang, D. Zhang X. Yong, and J. Yang, “Two-dimensional Discriminant Transform for Face Recognition,” Pattern Recognition, vol. 38, no. 7, pp. 1125-1129, Feb. 2005.
[27] W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld, “Face recognition: a literature survey,” ACM Computing Surveys, vol. 35, no. 4, pp. 399-458, Dec. 2003.