臺灣博碩士論文加值系統

本文目的在於開發應用程式在Android智慧型手機上應用，系統包含人臉偵測和人臉辨識兩部分。第一部分為人臉偵測，第二部分為人臉辨識，因為人臉偵測是人臉辨識的前置作業，其結果足以影響整個系統的效能，所以簡易人臉偵測和辨識，極為重要。由於本應用程式是即時系統，如何準確、快速地定位出人臉區域是開發辨識系統的主要目標。當影像輸入時，系統先利用顏色資訊從背景中分離出可能是人臉存在的膚色區塊，接著利用人臉區域中存在眼睛和嘴唇的區塊特徵，擷取出並改良之，然後再利用的人臉的幾何關係標定出正確的人臉位置，再以主成分分析的方法，簡化資料，再進行人臉辨識。

The purpose of this system is to develop an application on Android smartphone. The system consists of face detection and face recognition. The first part is to detect face. The second part is face recognition. Before face recognition, we start face detection. The efficient of face detection will affect the results of overall system performance. The system is a real-time system, the main factor of face detection is speed and accurate. First, the system separates the human face candidates from the background by color information when smartphone’s camera catch image. The system found the eyes and lip candidates from face candidates by feature methods. Next, the system locates the real face region using color information and the geometrical relation of eyes. Finally,the recognition process is verified by applying PCA algorithm.

摘要 I
ABSTRACT II
致謝 III
目錄 IV
圖目錄 VII
表目錄 X
第一章 緒論 1
1.1 前言 1
1.2 文獻探討 2
1.3 主要成果和貢獻 5
1.4 論文架構 5
第二章 系統描述 6
2.1 系統架構 6
2.1.1 人臉偵測 7
2.1.2 人臉參數訓練 8
2.1.3 人臉辨識 9
2.2 ANDROID簡介 10
2.3 模擬平台簡介 12
第三章 人臉偵測 14
3.1 色彩空間 14
3.2 膚色偵測 16
3.3 二值化處理 18
3.4 形態學處理 19
3.4.1 侵蝕 19
3.4.2 擴張 20
3.4.3 連通標記法 22
3.5 尋找人眼特徵 24
3.6 尋找嘴唇特徵 25
3.7 人臉定位 27
3.8 雙線性內插法 31
第四章 人臉辨識 33
4.1 主成分分析之簡介 33
4.2 主成分分析法之應用 35
4.3 人臉參數訓練 38
4.4 人臉辨識 41
第五章 實驗結果和討論 43
5.1 辨識率探討 43
5.1.1 光線不同實驗 44
5.1.2 背景不同實驗 48
5.1.3 閉眼實驗 50
5.1.4 手勢干預實驗 51
5.1.5 Caltech人臉實驗 52
5.2 討論 53
第六章 結論與未來展望 54
6.1 結論 54
6.2 未來展望 55
參考文獻 56
附錄一 59
附錄二 70
文章發表 87

[1]Sang-Hoon Kim and Hyoung-Gon Kim, “Face Detection using Multi-model Information”, IEEE Conference on Automatic Face and Gesture Recognition, pp. 14-19, 2000
[2]Sanjay Kr. Singh, D. S. Chauhan, Mayank Vatsa and Richa Singh,“A Robust Skin Color Based Face Detection Algorithm”, Tamkang Journal of Science and Engineering, pp. 227-234, 2003
[3]Kin Choong Yow and Roberto Cipolla, “Feature-based human face detection”, Image and Vision Computing, pp. 713-735, 1997
[4]C. Kotropoulos., “Rule-based face detection in frontal views”, IEEE Conference on Acoustics, Speech, and Signal Processing, pp. 2537- 2540, 1997
[5]C. Schneider, N. Esau, L. Kleinjohann and B. Kleinjohann, “Feature based Face Localization and Recognition on Mobile Devices”, IEEE Conference on Control, Automation, Robotics and Vision, pp.1-6, 2006
[6]P. Sinha, “Object recognition via image invariants: A case study”, Investigative Ophthalmology and Visual Science, pp. 1735-1740, 1994.
[7]A. Tsukamoto, C.-W. Lee and S. Tsuji, “Detection and tracking of human face with synthesized templates”, IEEE Conference on Computer Vision, pp. 183-186, 1993.
[8]Jin Tan, “Android English Dictionary Development Based on Intelligence Mobile Phone Platform”, IEEE Conference on E-Business and E-Government (ICEE), pp. 3741-3744, 2010
[9]J.C.T. Mawafo, W.A. Clarke and P.E. Robinson, “Identification of Facial Features on Android Platforms”, IEEE Conference on Industrial Technology, pp. 25-28,2013
[10]R.C. Gonzalez, R.E. Woods, “Digital Image Processing Third Edition”, Addison-Wesley Publishing, NY, 2007.
[11]Rein-Lien Hsu, M.A. Mottaleb and A.K. Jain, ” Face detection in color images”, IEEE Transactions on Pattern Analysis and Ma-chine Intelligence, Vol 24, no 5, pp. 696 – 706, 2002
[12]Shiping Zhu and Nan Zhang, “Face Detection Based on Skin Color Model and Geometry Features”, IEEE Conference on In-dustrial Control and Electronics Engineering, pp. 23-25, Aug. 2012
[13]Dewi Agushinta R, Adang Suhendra, Sarifuddin Madenda and Suryadi H.S., “Face Component Extraction Using Segmenation Method on Face Recognition System”, Journal of Emerging Trends in Computing and Information Sciences, pp. 67-72, 2011
[14]Ye-Peng Guan, “Unsupervised human height estimation from a single Image”, Journal of Biomedical Science and Engineering, pp.425-430, 2009
[15]Y. Jefferson, “Facial beauty: establishing a universal standard”, International Journal of Orthodontics, pp. 9-22, 2004
[16]P.N. Belhumeur, J.P. Hespanha and D. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol 19, no 7, pp. 711-720, 1997
[17]林青山，「多變項分析統計法」，東華書局，2013。
[18]S.J. Lee and S.B. Jung, ‘‘Face detection and recognition using PCA’’, IEEE Conference on TENCON 99, pp. 84-87,1999
[19]A.S. Khan and L.K. Alizai, “Introduction to Face Detection Us-ing Eigenfaces,” IEEE Conference on Emerging Techn ologies, pp. 128-132, 2006
[20]M.A. Turk and A.P. Pentland, “Face recognition using eigenfac-es”, IEEE Conference on Computer Vision and Pattern Recogni-tion, pp. 586-591, 1991
[21]網路資料: 加州理工大學人臉資料庫 http://www.vision.caltech.edu/html-files/archive.html