淡江大學覺生紀念圖書館 (TKU Library)
進階搜尋


下載電子全文限經由淡江IP使用) 
系統識別號 U0002-1407200808254300
中文論文名稱 應用二維主成分分析之人臉辨識
英文論文名稱 Face Recognition Using 2DPCA
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 96
學期 2
出版年 97
研究生中文姓名 白逸群
研究生英文姓名 I-Chun Pai
學號 695410323
學位類別 碩士
語文別 中文
第二語文別 英文
口試日期 2008-06-18
論文頁數 38頁
口試委員 指導教授-林慧珍
委員-許秋婷
委員-顏淑惠
委員-林慧珍
中文關鍵字 人臉辨識  主成份分析  離散餘弦轉換  權重投票法  空間域  頻率域  基因演算法 
英文關鍵字 Face Recognition  Principle Component Analysis(PCA)  Two Dimension Principle Component Analysis(2DPCA)  Discrete Cosine Transformation(DCT)  Weight-Voting  spatial domain  frequency domain 
學科別分類 學科別應用科學資訊工程
中文摘要 人臉辨識(Face Recognition)在模式識別領域中一直是個很重要的主題,它將影像、圖片或是攝影機中偵測出來的人物,識別出其身分。應用範圍包含「數位監控系統」、「門禁管理」、「智慧型人機互動」、「犯罪偵查」、「出入口管制」、「個人化的服務系統」等。基於所取得的人臉影像在傾斜角度、場景光線、髮型或是表情都可能有不同的呈現,人臉辨識系統須要面對許多不同的問題與挑戰。
本研究分析目前已被提出的多種人臉辨識方法,並提出一個人臉辨識模組,稱為Enhanced-2DPCA(簡稱E-2DPCA)。將E-2DPCA方法與分析中最高辨識率的兩個方法(分別利用2DPCA與DCT coefficient)比較,結果顯示E-2DPCA的平均辨識率雖然比另外兩個方法的平均辨識率還高,三種方法對不同測試影像各有不同優劣表現,因而我們結合三種方法,利用權重式投票法取得最後辨識結果。最後結果顯示,結合的方法可更進一步改進平均辨識率。
英文摘要 Face Recognition is an important topic in the field of pattern recognition. This technology has a variety of applications including entrance guard control, personal service system, criminal verification, and security verification of finance. Our research focuses on the development of a human face recognition system. To correctly identify a human in an image is a challenge due to various possible factors, including different light conditions, change of haircut, variation of face expression, and different aspects of the face.

We analyzed several existing face recognition techniques and found that each of them performs well over some sets of testing samples but poorly over some other sets. This motivated us to combine some different techniques to construct a better face recognition system. First, we proposed a new module, called Enhanced-2DPCA (or E-2DPCA). The accuracy of E-2DPCA is better than all the techniques we have analyzed. We chose the best two from those analyzed and compared them with our proposed E-2DPCA module, and found that although the E-2DPCA module outperforms the other two modules, each of the three modules behaves better than others over some set of samples. Thus we combine the three modules and apply weighted voting scheme to choose the recognition result from the results given by the three modules. Experimental results show that the integrated system can further improve the recognition rate.
論文目次 目錄
目錄 I
圖目錄 III
表目錄 V
第一章 緒論 1
1.1 研究動機與目的 1
1.2 系統流程 2
1.3 章節組織 4
第二章 人臉辨識簡介 5
2.1 人臉辨識 5
2.2 人臉辨識之相關研究 7
第三章 離散餘弦轉換與主成份分析 11
3.1 離散餘弦轉換 11
3.2 主成份分析 14
第四章 人臉辨識系統 20
4.1人臉辨識模組 20
4.2權重式投票法 22
4.3權重訓練 23
第五章 實驗結果與分析 24
第六章 結論與未來研究 28
參考文獻 29
附錄—英文論文 31

圖目錄
圖1. 訓練流程圖 2
圖2. 測試流程圖 3
圖3. 以上下兩組人臉影像為例,(a)為灰階人臉影像, (b)為經過權重調整後的影像,(c)為二值化後的影像 8
圖4. PCA(紅)與LDA(藍)在相同資料分佈(兩類資料,每類兩個資料點)下,主成份以紅色與藍色線條表示,DPCA與DLDA為在PCA與LDA方法下所形成之區別兩類資料的分隔線段 10
圖5. DCT參數所代表之頻率關係 11
圖6. DCT參數與影像邊緣(edge)的關係 12
圖7. (a) 影像在空間域的數值,(b) 影像在頻率域的數值,其中左上方紅框內為DC值,其它為AC值 13
圖8. 左圖為一個資料分佈,右圖為其分佈所計算之eigenvalue與eigenvector,紅藍兩向量為第一與第二個eigenvector,其長度代表其eigenvalue之大小 14
圖9. (a)原影像、(b)每個block取20個DCT係數後,使用IDCT運算後形成之影像、(c)原影像與(b)強化後影像(a=0.5) 21
圖10. Yale database的人臉影像範例 24
圖11. Yale database中,不同的表情與光源效果 25
圖12. 以8x8影像區塊進行DCT處理的結果,X軸為保留的係數個數,Y軸為2DPCA辨識成功率。可以發現在保留係數個數為20個時,其成功率有最好的表現 25
圖13. 三個辨識模組與權重投票法利用不同影像組訓練後之辨識率比較 27

表目錄
表1. 基因演算法最佳化處理後的所得到之權重 27
表2. 三個辨識模組與權重投票法的平均辨識率比較 27

參考文獻 1. Matthew A. Turk and Alex P. Pentland, “Face Recognition Using Eigenfaces,” Computer Vision and Pattern Recognition, pp. 586-591, 1991.
2. P. Yampri, C. Pintavirooj, S. Daochai, and S. Teartulakarn, “White Blood Cell Classification based on the Combination of Eigen Cell and Parametric Feature Detection,” Papers of Technical Meeting on Medical and Biological Engineering, Vol. 6, No. 95-115, pp. 1-4, 2006.
3. Kazuma Shigenari, Fumihiko Sakaue, and Takeshi Shakunaga, “Decomposition and Virtualization of Eigenface for Face Recognition under Various Lighting Conditions,” Systems and Computers in Japan, Vol. 36, No. 1, pp. 25-34, 2005.
4. M. Anouar Mellakh, Dijana Petrovska-Delacretaz, and Bernadette Dorizzi, “Using Signal/Residual Information of Eigenfaces for PCA Face Space Dimensionality Characteristics,” in Proceeding of the 18th International Conference on Pattern Recognition (ICPR‘06), Vol. 4, pp. 574-577.
5. Kwan-Ho Lin, Kin-Man Lam, and Wan-Chi Siu, “Spatially Eigen-weighted Hausdorff Distances for Human Face Recognition,” Pattern Recognition 36, No. 8, pp. 1827-1834, August 2003.
6. Jia-Zhong He, Qing-Huan Zhu, and Ming-Hui Du, “Face Recognition Using PCA on Enhanced Image for Single Training Images,” in Proceedings of the Fifth International Conference on Machine Learning and Cybernetics, pp. 3218-3221, August 13-16, 2006.
7. Xiaopeng Hong, Hongxun Yao, Yuqi Wan, and Rong Chen, “A PCA based Visiual DCT Feature Extraction Method for Lip-Reading,” in Proceeding of the 2006 International Conference on Intelligent Information Hiding and Multimedia Signal Proceeding (IIH-MSP’06), pp. 321-326, December 2006.
8. Jian Yang, David Zhang, Alejandro F. Frangi, and Jing-yu Yang, “Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 1, pp. 131-137, January 2004.
9. Ying Wen and Pengfei Shi, “Image PCA: A New Approach for Face Recognition,” International Conference on Acoustics, Speech, and Signal Processing, Vol. 1, pp. 1-1241-1-1244, April 2007.
10. Wenyu Sun and Qiuqi Ruan, “Two-Dimension PCA for Facial Expression Recognition,” the 8th International Conference on Signal Processing, Vol. 3, 2007.
11. Daoqiang Zhang and Zhi-Hua Zhou, “(2D)2PCA: 2-Directional 2-Dimensional PCA for Efficient Face Representation and Recognition,” Neurocomputing, Vol. 69, pp. 224-231, 2005.
12. R. A. Fisher, “The Statistical Utilization of Multiple Measurements,” Annals of Eugenics, Vol. 8, pp. 376-386, 1938.
13. Aleix M. MartõÂnez and Avinash C. Kak, “PCA versus LDA,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 2, pp. 228-233, February 2001.
14. Jia-Zhong He, Ming-Hui Du, Sheng-Wei Pei, and Quan Wan, “Face Recognition Based on Spectroface and Uniform Eigen-Space SVD for one Training Image per Person,” in Proceeding of the Fourth International Conference on Machine Learning and Cybernetics, Vol. 8, pp. 4842-4845, August 2005.
15. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Professional, 1989.
16. Yale database at http://cvc.yale.edu/projects/yalefaces/yalefaces.html

論文使用權限
  • 同意紙本無償授權給館內讀者為學術之目的重製使用,於2008-07-17公開。
  • 同意授權瀏覽/列印電子全文服務,於2008-07-17起公開。


  • 若您有任何疑問,請與我們聯絡!
    圖書館: 請來電 (02)2621-5656 轉 2281 或 來信