簡易方法之少量人臉辨識系統

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：63

、訪客IP：18.226.163.123

姓名

楊煒達(Wei-da Yang) 查詢紙本館藏

畢業系所

資訊工程學系在職專班

論文名稱

簡易方法之少量人臉辨識系統
(A simple approach to a small-scaled face recognition system)

相關論文

★ 以Q-學習法為基礎之群體智慧演算法及其應用	★ 發展遲緩兒童之復健系統研製
★ 從認知風格角度比較教師評量與同儕互評之差異：從英語寫作到遊戲製作	★ 基於檢驗數值的糖尿病腎病變預測模型
★ 模糊類神經網路為架構之遙測影像分類器設計	★ 複合式群聚演算法
★ 身心障礙者輔具之研製	★ 指紋分類器之研究
★ 背光影像補償及色彩減量之研究	★ 類神經網路於營利事業所得稅選案之應用
★ 一個新的線上學習系統及其於稅務選案上之應用	★ 人眼追蹤系統及其於人機介面之應用
★ 結合群體智慧與自我組織映射圖的資料視覺化研究	★ 追瞳系統之研發於身障者之人機介面應用
★ 以類免疫系統為基礎之線上學習類神經模糊系統及其應用	★ 基因演算法於語音聲紋解攪拌之應用

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

人臉辨識可以廣泛的應用在各個不同的領域之中，比方大樓門禁管制、犯罪人員辨識、金融提款的安全驗證，以及網路交易的身份識別等，都可以使用，不僅可靠，而且快速方便。
本論文使用結合PCA與臉部特徵的人臉辨識，其中包含使用PCA-based求出Eigenvalue、Eigenvector和Eigenspace，再藉由投影，得到輸入Sample與各訓練人臉的距離。此外，還藉由投影和邊緣偵測求得雙眼和嘴巴三個區域，以及雙眼、眼角和嘴巴七個特徵點，最後做正規化和特徵區域比對，然後與訓練之人臉做辨識。
結果驗證部分，本實驗使用Cohn Kanade Database，裡面包含了九十七位不同的人，每人各選十張，分成九組，其辨識率皆為100%。並且另外自行拍攝十個人，共六十張照片，來做訓練和辨識之用，其結果也皆可達到100%。

摘要(英)

The Face recognition can be applied to many different fields. For example, the building entrance guard control, the criminal verification, the security verification of finance, and the identity verification of the network trade, etc. The face recognition is not only reliable but also fast and convenient.
This disquisition combines PCA with face features for face recognition, including using PCA-based to figure out the Eigenvalue, Eigenvector and Eigenspace. Then get the distance between sample image and training image by the projection. Besides, we can get the eye and mouth areas, and the feature points of the eyes, canthi and the mouth by using the projection and edge detection. And finally, we do the normalization and area comparison to recognize the input image.
The result verification. we use the Cohn Kanade Database that includes ninety-seven different persons. Ten pictures each person and we divide them into nine sets. All of the recognition rates are 100%. In addition, we make a face image database by ourselves. That includes ten different persons and sixty pictures to do the training and recognition. The recognition rate is also 100%

關鍵字(中)

★ 人臉辨識
★ 特徵空間投影
★ 特徵空間

關鍵字(英)

★ eigenvector
★ face recognition
★ PCA
★ eigenvalue
★ eigenspace

論文目次

摘要 i
Abstract ii
目錄 v
圖目錄 viii
表目錄 x
第一章緒論1
1.1 研究動機1
1.2 研究目的2
1.3 論文架構3
第二章人臉辨識介紹與相關研究之探討4
2.1 人臉辨識介紹4
2.2 人臉偵測9
2.2.1 方法介紹11
2.3 PCA介紹14
2.3.1 Eigenvalue and Eigenvector18
2.3.2 特徵空間19
2.3.3 特徵空間投影20
2.3.4 PCA之缺點21
2.4 OpenCV介紹23
第三章訓練與辨識之方法與步驟25
3.1 人臉特徵區域擷取(幾何特徵)25
3.1.1 特徵區域擷取25
3.1.2 特徵區域最佳化26
3.1.2.1灰階轉換26
3.1.2.2二值化處理27
3.1.2.3特徵區域最佳化27
3.2 人臉特徵點擷取(幾何特徵)32
3.2.1 眼角特徵點擷取32
3.2.2 眼睛特徵點擷取34
3.2.3 嘴巴特徵點擷取37
3.3 特徵區域比對(幾何特徵)39
3.4 PCA特徵擷取(統計特徵)39
3.5 人臉辨識系統架構40
3.6 訓練方法及步驟42
3.7 辨識方法及步驟43
3.7.1 辨識結果統計方法43
3.7.2 辨識流程45
第四章實驗結果與分析47
4.1 人臉資料庫47
4.1.1 ORL人臉資料庫47
4.1.2 中研院人臉資料庫48
4.1.3 Yale人臉資料庫48
4.1.4 Stirling人臉資料庫49
4.1.5 Cohn Kanade人臉資料庫49
4.1.6 自製人臉資料庫50
4.2 實驗環境50
4.3 相關實驗辨識結果52
4.3.1 Face recognition committee machine52
4.3.2 Robust face recognition using minimax
probability machine54
4.3.2 A Method For Improved PCA in Face Recogntion 55
4.4 本實驗辨識結果55
第五章結論與未來展望58
5.1 結論59
5.2 未來展望60
參考文獻 61
附錄一 ORL人臉資料庫部分人臉資料68
附錄二中研院人臉資料庫部分人臉資料69
附錄三 Yale人臉資料庫部分人臉資料70
附錄四 Stirling人臉資料庫部分人臉資料71
附錄五 Cohn Kanade人臉資料庫部分人臉資料72
附錄六自製人臉資料庫部分人臉資料73

參考文獻

[1] O. Arandjelovic and A. Zisserman, “Automatic face recognition for film character retrieval in feature-length films”, IEEE Computer Society Conference on Volume 1, 20-25 June 2005, pp.860 – 867
[2] R.J. Baron, “Mechanisms of human facial recognition”, Int. J. Man-Machine Studies, 1981.
[3] P.N Belhumeur, J.P. Hespanha, and D.J. Kriegman,, "Eigenfaces vs. Fisherfaces: recognition using class specific linear projection," IEEE Transactions, Pattern Analysis and Machine Intelligence, 1997
[4] P.N. Belhumeur and D.J. Kriegman, “What is the set of images of an object under all possible lighting conditions?” In Proceedings, IEEE Conference on Computer Vision and Pattern Recognition. 52–58, 1997.
[5] S. Baker, I. Matthews and J. Schneider, “Automatic construction of active appearance models as an image coding problem, Pattern Analysis and Machine Intelligence”, IEEE Transactions, Volume: 26, Issue: 10 On pp. 1380- 1384, 2004
[6] R. Brunelli and T. Poggio, ”Face Recognition: Feature versus Templates.”, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.15, no. 10, 1993.
[7] V. Blanz and T. Vetter, “Face recognition based on fitting a 3D morphable model”, IEEE Trans. PAMI, 2003, vol. 25, no. 9, pp. 1063-1074.
[8] C.P. Chen and C.S. Chen, “Lighting Normalization with Generic Intrinsic Illumination Subspace for Face Recognition,” accepted by IEEE International Conference on Computer Vision, ICCV 2005, Beijing, China, October 2005
[9] A. J. Colmenarez and T. S. Huang, “Face detection with information based maximum discrimination,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 782-787, 1997.
[10] M.J. Er, W. Chen, and S. Wu, “High-Speed Face Recognition Based on Discrete Cosine Transform and RBF Neural Networks”, IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 16, NO. 3, MAY 2005
[11] R. F´eraud, O.J. Bernier, J.E. Viallet, and M. Collobert, “A Fast and Accurate Face Detection Based on Neural Network,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 1, pp. 42-53, Jan. 2001.
[12] Y. Freund and R.E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, vol.55, pp. 119-139, 1997.
[13] G. Guo, S.Z. Li and K. Chan, “Face Recognition by Support Vector Machines”, Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000 pp. 196
[14] C. Garcia and G. Tziritas, “Face detection using quantized skin color regions merging and wavelet packet analysis,” IEEE Transactions on Multimedia, vol. MM-1, no. 3, pp. 264-277, Sept. 1999.
[15] R.C. Gonazlez and R.E. Woods, Digital image processing. 2nd. Addison-wesley, 1992.
[16] R.L. Hsu, M. Abdel-Mottaleb, and AK. Jain, "Face Detection in Color Images," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 696-706, 2002.
[17] B. Heisele, P. Ho, J. Wu, and T. Poggio, “Face recognition: component-based versus global approaches”, Computer Vision and Image Understanding, 91(1):6–12, 2003.
[18] C.H. Hoi and M.R. Lyu, “Robust face recognition using minimax probability machine”, 7th International Conference on Automatic Face and Gesture Recognition, 2006
[19] V.V. Kohir and U.B. Desai, “Face recognition using a DCT-HMM approach,” in Proc. IEEE Workshop on Applications of Computer Vision (WACV’98), Princeton, NJ, 1998, pp.226–231.
[20] M. Kirby and L. Sirovich, ”Application of the Karhunen-Loeve procedure for the characterization of human faces”. IEEE Trans. Patt. Anal. Mach. Intell. 12, 1990.
[21] K.C. Lee, J. Ho, M.H. Yang and D. Kriegman, “Video-Based Face Recognition Using Probabilistic Appearance Manifolds”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '03) - Volume 1, 2003, pp. 313
[22] K.C. Lee, J. Ho, M.H. Yang and D. Kriegman , “Visual tracking and recognition using probabilistic appearance manifolds”, Computer Vision and Image Understanding 99, 2005, pp.303–331
[23] D. Maio and D. Maltoni, “Real-time Face Location on Gray-scale Static Images,” Pattern Recognition, vol. 33, no. 9, pp. 1525-1539, Sept. 2000.
[24] B. Moghaddam and A. Pentland, ”Face Recognition using View-Based and Modular Eigenspaces”, Automatic Systems for the Identification and Inspection of Humans, SPIE 1994.
[25] Z. Pan, R. Adams, and H. Bolouri, “Dimensionality reduction of face images using discrete cosine transforms for recognition.” submitted to IEEE Conference on Computer Vision and Pattern Recognition, 2000.
[26] Z. Pan and H. Bolouri, “High Speed Face Recognition Based on Discrete Cosine Transforms and Neural Networks”, 1999
[27] H. A. Rowley, S. Baluja, and T. Kanade, “Neural Network-Based Face Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 23-38, Jan. 1998.
[28] H. A. Rowley, S. Baluja, and T. Kanade, “Rotation Invariant Neural Network-Based Face Detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 38-44, 1998.
[29] T.R. Raviv and A. Shashua, “The quotient image: Class based re-rendering and recognition with varying illuminations”. In Proceedings, IEEE Conference on Computer Vision and Pattern Recognition. 566–571, 1999.
[30] M. Safari, M.T. Harandi and B.N. Araabi, “A SVM-based method for face recognition using a wavelet PCA representation of faces”, Image Processing. ICIP '04. 2004 International Conference on Volume 2, 24-27 Oct. 2004 pp.853 - 856 Vol.2
[31] K.K. Sung and T. Poggio, “Example-Based Learning for View-Based Human Face Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 39-51, Jan. 1998.
[32] M.A. Turk and A.P. Pentland, "Face recognition using eigenfaces," presented at Computer Vision and Pattern Recognition, 1991.
[33] H.M. Tang, M.R. Lyu and I. King, “Face recognition committee machine”, 7th International Conference on Automatic Face and Gesture Recognition, 2006
[34] P. Viola and M.J. Jones, "Rapid Object Detection using a Boosted Cascade of Simple Features," in Proceedings of the IEEE Computer Society International Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511-518, Dec. 2001.
[35] T. Vetter and T. Poggio, “Linear object classes and image synthesis from a single example image”. IEEE Trans. Patt. Anal. Mach. Intell. 19, 733–742, 1997.
[36] H. Wu, Q. Chen, and M. Yachida, “Face Detection From Color Images Using a Fuzzy Pattern Matching Method,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 6, pp. 557-563, June 1999.
[37] L. Wiskott, J.M. Fellous and C.V.D. Malsburg, “Face recognition by elastic bunch graph matching”. IEEE Trans. Patt. Anal. Mach. Intell. 19, 775–779, 1997.
[38] H.T. Wang, S.Z. Li, Y.S. Wang, “Face Recognition under Varying Lighting Conditions Using Self Quotient Image”, Automatic Face and Gesture Recognition. Proceedings. Sixth IEEE International Conference on Publication, 17-19 May 2004
[39] M.H. Yang and N. Ahuja, “Detecting Human Faces in Color Images,” in Proceedings of the IEEE International Conference on Image Processing, pp. 127-139, Oct. 1998.
[40] K.C. Yow and R. Cipolla, “Feature-based Human Face Detection,” Image and Vision Computing, vol. 15, no. 9, pp. 713-735, Sept. 1997.
[41] M. H. Yang, D. Kriegman, and N. Ahuja, “Detecting Faces in Images: A survey,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 34-58, Jan.2002.
[42] J. Yang, D. Zhang, A.F. Frangi, and J.J.Y Yang, ”Two-Dimensional PCA: A New Approach to Representation and Recognition”, IEEE Transactions on pattern analysis and machine intelligence, pp.131-137, 2004.
[43] D.P. Zhou, J.Y. Gan and C.Z. Li, “A Method For Improved PCA in Face Recognition”, International Journal of Information Technology, Vol. 11, no. 11, 2005.
[44] J. Zhu, M.I. Vai and P.U. Mak, “Face Recognition Using 2D DCT with PCA", in The 4nd Chinese Conference on Biometric Recognition (Sinobiometrics’03) at Beijing, P. R. China, Dec. 7-8, 2003.
[45] W. Zhao and R. Chellappa, “SFS Based View synthesis for robust face recognition”. In Proceedings, International Conference on Automatic Face and Gesture Recognition, 2000.
[46] 李秉翰, “電腦視覺監控產學研聯盟電子報”, 電腦視覺監控產學研聯盟, 第七期, Jan. 2006.
[47] 謝怡竹, “An Optical-Flow Based Automatic Expression Recognition System”, National Central University, Master Paper, 2005.
[48] 熊昭岳, “An Attention Detection System for Vehicles”, National Central University, Master Paper, 2005.

指導教授

蘇木春(Mu-chun Su)

審核日期

2007-7-5

推文