臺灣博碩士論文加值系統

本論文提出一套車用保全人臉識別系統，主要應用影像處理技術及興趣點偵測，並結合衛星導航系統及行動電話，改善目前被動式防盜之缺失。研究中利用人臉五官特徵之不同，將擷取出的有效影像進行前處理後，結合興趣點偵測及幾何雜湊距離，計算出一組特徵值予以儲存於資料庫中，供後續步驟進行入侵偵測比對。本研究主要分成四個階段來進行，第一階段主要透過影像前處理將所粹取之影像予以正規化，第二階段針對所擷取出來之特徵值個別進行興趣點的粹取，第三階段將所粹取出來之興趣點利用幾何雜湊距離計算出各特徵值並將假設合法者之特徵值存入資料庫中，最後將測試影像予以比對並統計出準確度。本論文所提出的辨識系統可在不同天候不同光源的環境中，皆可於既定的辨識速度下，產生高辨識率的輸出。實驗結果顯示，在430個測試樣本，共4600張測試影像中，本論文提出的車用保全系統平均辨識準確度可達91.33%，可以作為日後車用和家用保全市場發展主動式防盜技術之參考。

In this thesis, we propose an embedded telematics system for driver identification and positioning based on 3G mobile system. Image processing techniques and detection of interest points are used as the basis of recognition for driver identification. The warning messages with GPS information are sent automatically through the 3G mobile phone technologies to overcome the weaknesses of current passive anti-theft technologies. There are four steps in our proposed method. First, the face region are detected and normalized by image preprocessing techniques. Secondly, the interesting points are extracted to represent the facial features. Thirdly, the Hash distance is evaluated according to the extracted interesting points. Finally, the matching results are used to identify the driver’s identity. The identification system proposed in this thesis provides a high-speed and accurate recognition result even in different weather or environments with different lights. There are 430 persons and 4600 images in our simulations. Experimental result shows that the accuracy of the proposed system in this thesis is 91.33%, and it can be used to develop the vehicle-use and household-use active anti-theft technology effectively in the future.

書名頁 i
論文口試委員審定書 ii
授權書 iii
中文摘要 iv
英文摘要 v
誌 謝 vi
目 錄 vii
表目錄 x
圖目錄 xi

第一章 緒論 1
1.1 研究背景 1
1.2 研究動機與目的 2
1.3 研究架構 4
第二章 文獻探討 5
2.1 第三代行動通訊系統 5
2.1.1 第三代行動通訊技術規格 7
2.1.2 第三代行動通訊(3G)市場發展現況與應用服務 9
2.2 全球衛星定位系統 14
2.2.1 全球衛星定位系統 (GPS)架構 15
2.2.2 全球衛星定位系統 (GPS)市場概況與應用 16
2.3 人臉辨識 18
2.4 嵌入式行動監視定位系統 20
2.5 嵌入式汽車行動監視定位系統流程 22
第三章 影像前處理與人臉偵測 24
3.1 影像前處理與人臉偵測之系統流程 24
3.2 影像前處理 24
3.2.1 光源補償 25
3.2.2 膚色偵測 26
3.2.3 灰階化 33
3.2.4 影像二值化 34
3.2.5 腐蝕與膨脹 34
3.2.6 灰階群組群組法 38
3.2.7 中值濾波 44
3.2.8 取得人臉位置及比例切割 45
第四章 特徵值取得與興趣點投票 46
4.1特徵值取得與興趣點投票之流程圖 46
4.2 特徵值取得 47
4.2.1 邊緣偵測 47
4.2.2 水平與垂直投影 50
4.2.3 眼睛定位 53
4.2.4 嘴巴定位 57
4.2.5 鼻子定位 58
4.3 興趣點之取得與投票 59
4.3.1 特徵點取得 62
4.3.2　距離法則 63
4.3.3　投票 64
第五章 實驗結果與討論 69
5.1 實驗環境 69
5.2 入侵偵測 71
5.3 人臉影像資料庫建立 72
5.4 人臉定位與特徵值擷取結果 73
5.5 傳送可疑人物影像 74
第六章 結論 76
參考文獻 78

[1]3G通訊推升車用資通服務ITS成為熱門應用，網址http://www.eettaiwan.com/ART_8800457839_675327_NT_9a635fe3.htm，電子工程專輯
[2]M. A. P. Turk, A.P., “Face recognition using eigenfaces,” Computer Vision and Pattern Recognition, vol.3 no.6, pp 586- 591, Jun. 1991.
[3]P. N. Belhumeur, J.P. Hespanha, and D.J Kriegmen., “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vo.19 no.7, pp711-720, Jul. 1997.
[4]Roberto Brunelli, Tomaso Poggio., “Face Recognition: Feature versus Templates”, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol.15 no.10, pp.1042-1052, Oct. 1993.
[5]Moghaddam, B. and A. Pentland., “Face Recognition using View-Based and Modular Eigenspaces”, Automatic Systems for the Identification and Inspection of Humans, SPIE vol.2277,pp.12-21, Oct. 1994.
[6]Bernd Heisele, Purdy Ho, Jane Wu, and Tomaso Poggio., “Face recognition: component-based versus global approaches”, Computer Vision and Image Understanding, vol. 91 no.1, pp.6-21, Feb. 2003.
[7]連顥尹，「泛3G 標準之發展與趨勢」，拓墣產業研究所，Jan. 2008.
[8]C. Lin, K.-C. Fan., “Human Face Detection Using Triangle Relationship,” Proceedings of 15th International Conference on Pattern Recognition. Barcelona, Spain, vol.2, pp.941-944, Sept.2000.
[9]Garcia, C., Tziritas, G., “Face Detection Using Quantized Skin Color Regions Merging and Wavelet Packet Analysis,” IEEE Transactions on Multimedia, vol.1 no.3, pp. 264-277, Sept. 1999.
[10]Hsu, R. L., A. M. Mohamed and A. K. Jain., “Face detection in color image”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24 no.5, pp.696-704, May 2002.
[11]Q. Yuan, W. Gao, H. Yao., “ Robust Frontal Face Detection in Complex Environment ”, Proceedings of 16th International Conference on Pattern Recognition, vol.no.1, pp.25-28, Aug. 2002.
[12]Yang, G. and T. S. Huang, “Human face detection in complex background”, Pattern Recognition, vol.27 no.1, pp.53-63, Jan.1994.
[13]黃登淵，莊國楨，楊晏和，陳南樺，王嘉宏，「複雜背景下多重人臉偵測演算法之研究」，科學與工程技術期刊第三卷 第三期 ，2007
[14]Terrillon, J. C., M. David and S. Akamatsu., “Detection of human faces in complex scene images by use of a skin color model and invariant fourier-mellin moments”, Proceedings of the 14th International Conference on Pattern Recognition, Brisbane, Australia, Aug. 1998.
[15]Zhao, L. H., X. L. Sun, J. H. Liu and X. H. Xu., “Face detection based on skin color”, Proceedings of the Third International Conference on Machine Learning and Cybemetics, Shanghai, China, Aug. 2004.
[16]Soriano, M., B. Martinkauppi, S. Huovinen and M. Laaksonen., “Skin detection in video under changing illumination conditions”, Proceedings of the 15th International Conference on Pattern Recognition, Barcelona, Spain, Sept. 2000.
[17]ZhiYu Chen, Besma R. Abidi, David L. Page, and Mongi A, Abidi., “Gray-level grouping (GLG): an automatic method for optimized image contrast enhancement-part I: the basic method”, IEEE Transaction on Image Process., vol. 15 no.8, pp. 2290-2302, Aug. 2006.
[18]Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, Addison, Wesley Publishing Co., 1992.
[19]R.O. Duda and P.E. Hart., “Use of the Hough transform to detect lines and curves in pictures”, Communications of the ACM, vol. 15 no.1, pp.11-15, Jan. 1972.
[20]Chiou-Ting Hsu, Ming-Chou Shih., “Content-Based Image Retrieval by Interest Points Matching and Geometric Hashing”, Proceedings of SPEE photomics. Asia Conference, vol.4925, pp.80-90, Shanghai, China, Oct. 2002.
[21]S. Smith, “A new class of corner finder”, Proceedings of the British Machine Vision
Conference, University of Leeds, UK. pp. 139-148, Sep. 1992.
[22]H. Z. Hel-Or, Y. Yitzhaki, and Y. Hel-Or., “Geometric Hashing Techniques for Watermarking,” Proceedings of 2001 International Conference on Image Processing. Thessaloniki, Greece, vol.2 pp. 498-501, Oct. 2001.