(3.238.186.43) 您好!臺灣時間:2021/02/26 12:40
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
研究生:張佑傑
論文名稱:於複雜背景及不同光影環境下之即時人臉偵測系統
論文名稱(外文):The Real-Time Face Detection System Under Complex Background and Varying Lighting Condition
指導教授:李忠謀李忠謀引用關係
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:資訊教育學系
學門:教育學門
學類:專業科目教育學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:52
中文關鍵詞:人臉偵測圖形識別高斯混合模型貝氏定理粗糙分類器梯度即時系統串連式架構
外文關鍵詞:Face DetectionPattern RecognitionGaussian mixtureBay's theormweak classifierAdaBoostGradientReal-Time SystemCascadeBioID
相關次數:
  • 被引用被引用:0
  • 點閱點閱:153
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
人臉偵測於近年來受到重視,並廣泛運用於各種領域,如:人臉身份辨識、人臉追蹤及以內容為主之影像檢索系統。此方面的研究,皆須偵測人臉並定位以進行後續的處理,因此如何精確並快速偵測人臉為相當重要之議題。本研究提出以梯度為主之即時人臉偵測系統,將偵測分為兩階段,第一階段以人臉及非人臉之梯度分佈高斯混合模型,並使用動態間隔偵測法,大幅降低需掃瞄之視窗數目,第二階段串連七個梯度空間相關性模型,進行人臉精確定位並有效移除誤判視窗,且保留人臉視窗。實驗證實,本研究所提出之梯度分佈特徵對臉部姿勢、表情、轉頭及傾斜有良好的強健性,並於複雜背景及光源變化等情況,仍可精確定位人臉,在實驗影像資料庫BioID及Viplab各達到91%及95%之偵測率,並維持極低的誤判視窗數目,且於Pentium M 1.5GHz之筆記型電腦上,每秒可處理10張320×240影像,亦滿足即時偵測之需求。
Human face detection is an important capabilities in a wide range of applications, such as face recognition, face tracking, and content-based image retrieve. Detecting and locating face in image is a necessary procedure before any future processing. We proposed a real-time face detection system including two gradient-based models. In first stage, two Gaussian mixtures of facial and non-facial weighted gradient distribution are used to roughly locate face in image. For accelerating detecting speed, dynamic interval detection algorithm is proposed to avoid redundant computations. In second stage, spatial gradient relation model is proposed to remove false detection and locate the facial positions precisely. In experimental results, weighted gradient distribution and spatial gradient relation model are proven to robust to different facial pose, expression, and rotation. Proposed methods can achieved detection rate of 91% and 95% respectively in database of BioID and Viplab under complex background and varying light condition. Proposed system can detect faces in 10 frames per second with size of 320×240 on a Intel Pentium M 1.5GHz notebook.
第一章 緒論...1
1.1 研究動機...1
1.2 研究目的...1
1.3 研究範圍與限制...2
1.4 論文架構...3

第二章 文獻探討...5
2.1 特徵法...5
2.1.1 臉部特徵...6
2.1.2 臉部材質...7
2.1.3 膚色...7
2.1.4 多種特徵...8
2.2 模版法...9
2.2.1 預先定義之樣版...9
2.2.2 可變形樣版...10
2.3 外觀法...11
2.3.1 臉部特徵根...12
2.3.2 特徵分佈...12
2.3.3 類神經網路...13
2.3.4 支持向量機...14
2.3.5 隱藏式馬可夫模型...15
2.4 問題討論...17

第三章 方法與步驟...22
3.1 梯度分佈資訊...23
3.2 最大期望法...25
3.3 動態間隔偵測法...27
3.4 梯度空間相關性模型...29
3.5 AbaBoost演算法...32
3.6 串連式分類器...34

第四章 實驗結果與討論
4.1 實驗影像來源...35
4.2 模型訓練...35
4.3 參數調整...36
4.3.1 高斯分佈數目...36
4.3.2 門檻值與誤判率之關係...37
4.4 實驗驗證...39
4.4.1 成功偵測區域...39
4.4.2 Test Set 1實驗結果...39
4.4.3 Test Set 2實驗結果...40
4.4.4 偵測速度實驗...40
4.5 與其它系統比較...40
4.6 討論...42

第五章 結論與未來研究...45
5.1 結論...45
5.2 未來研究...46

參考文獻...47
[1] A. Pentland, “Perceptual Intelligence,” Communication of the ACM, vol. 43, no. 3, pp. 35-44, 2000.
[2] A. Pentland, T. Choudhury, “ Face recognition for smart environments,” IEEE Computer, pp. 50-55, 2000.
[3] K. Lam and H. Yan, “Fast Algorithm for Locating Head Boundaries”, Journal of Electronic Imaging, vol. 3, no. 4, pp. 351-359, 1994.
[4] 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.
[5] A. V. NeJian and M. H. Hayes, “Face Detection and Recognition Using Hidden Markov Models,” Proc. Int. Conf. Image Processing, vol. 1, pp. 141-145, 1998.
[6] M. H. Yamg, D. J. Kriegman, and N. Ahuja, “Detecting Faces in Image: A Survey”, IEEE Trans. Pattern and Machine Intelligence, vol. 24, no. 1, pp. 34-37, 2000.
[7] S.A. Sirohey, “Human Face Segmentation and Identification,” Technical Report CS-TR-3176, Univ. of Maryland, 1993.
[8] T.K. Leung, M.C. Burl, and P. Perona, “Finding Faces in Cluttered Scenes Using Random Labeled Graph Matching,” Proc. Fifth IEEE Int. Conf. Computer Vision, pp. 637-644, 1995.
[9] M.F. Augusteijn and T.L. Skujca, “Identification of Human Faces through Texture-Based Feature Recognition and Neural Network Technology,” Proc. IEEE Conf. Neural Networks, pp. 392-398, 1993.
[10] S. McKenna, S. Gong, and Y. Raja, “Modelling Facial Colour and Identity with Gaussian Mixtures,” Pattern Recognition, vol. 31, no. 12, pp. 1883-1892, 1998.
[11] R. Kjeldsen and J. Kender, “Finding Skin in Color Images,” Proc. Int. Conf. Automatic Face and Gesture Recognition, pp. 312-317, 1996.
[12] B. Fröba and C. Küblbeck, “Robust Face Detection at Video Frame Rate Based on Edge Orientation Features,” Proc. Int. Conf. Automati Face and Gesture Recognition, pp.327-332, 2002.
[13] A. Yuille, P. Hallinan, and D. Cohen, “Feature Extraction from Faces Using Deformable Templates,” Journal of Computer Vision, vol. 8, no. 2, pp. 99-111, 1992.
[14] M. Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
[15] 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.
[16] H. A. Rowely, 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.
[17] C. Garcia and M. Delakis, “Convolutional Face Finder: A Neural Architecture for Fast and Robust Face Detection,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 11, pp. 1408-1422, Nov. 2004.
[18] E. Osuna, R. Freund, and F. Girosi, “Training Support Vector Machines: An Application to Face Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 130-136, 1997.
[19] H. Jee, K. Lee, and S. Pan, “Eye and face detection using SVM,” Proc. IEEE Conf. Intelligent Sensors, Sensor Networks and Information, pp. 577-580, Dec. 2004.
[20] A. V. Nefian and M. H. Hayes, “Maximum Likelihood Training of The Embedded HMM For Face Detection and Recognition,” Proc. IEEE Int. Conf. Image Processing, vol. 1, pp. 33-36, Sep. 2000.
[21] Y. Dai and Y. Nakano, “Face-Texture Model Based on SGLD and Its Application in Face Detection in a Color Scene,” Pattern Recognition, vol. 29, no. 6, pp. 1007-1017, 1996.
[22] H. P. Graf, T. Chen, E. Petajan, and E. Cosatto, “Locating Faces and Facial Parts,” Proc. Int. Workshop Automatic Face and Gesture Recognition, pp. 41-46, 1995.
[23] Y. Miyake, H. Saitoh, H. Yaguchi, and N. Tsukada, “Facial Pattern Detection and Color Correction from Television Picture for Newspaper Printing,” Journal of Imaging Technology, vol. 16, no. 5, pp. 165-169, 1990.
[24] D. Chai and K. N. Ngan, “Locating Facial Region of a Head-and-Shoulders Color Image,” Proc. Int. Conf. Automatic Face and Gesture Recognition, pp. 124-129, 1998.
[25] Y. Raja, S. J. McKenna, and S. Gong, “Tracking and Segmenting People in Varying Lighting Conditions using Colour,” Proc. Int. Conf. Automatic Face and Gesture Recognition, pp. 228-233, Apr. 1998.
[26] K. Sobottka and I. Pitas, “Face Localization and Feature Extraction Based on Shape and Color Information,” Proc. IEEE Int. Conf. Image Processing, pp. 483-486, 1996.
[27] K. Anderson and P. W. McOwan, “Robust real-time face tracker for clustered environment,” Computer Vision and Image Understanding, vol. 95, no. 2, pp. 184-200, 2004.
[28] P. Sinha, “Perceiving and recognising three-dimensional forms,” Ph.D. Dissertation, MIT, Cambridge.
[29] D. Maio and D. Maltoni, “Real-Time face location on gray-scale static image,” Pattern Recognition, vol. 33, no. 9, pp. 1525-1539, 2000.
[30] Y. H. Kwon and N. da Vitoria Lobo, “Face Detection Using Templates,” Proc. Int. Conf. Pattern Recognition, pp. 764-767, 1994.
[31] A. Lanitis, C. J. Taylor, and T. F. Cootes, “An Automatic Face Identification System Using Flexible Appearance Models,” Image and Vision Computing, vol. 13, no. 5, pp. 393-401, 1995.
[32] 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.
[33] E. Osuna, R. Freund, and F. Girosi, “Training Support Vector Machines: An Application to Face Detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 130-136, 1997.
[34] J. A. Bilmes, “A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models,” UC-Berkeley TR-97-021, 1998.
[35] L. L. Huang, A. Shimizu, Y. Hagihara, and H. Kobatake, “Gradient Feature Extraction for Classification-based Face Detection,” Pattern Recognition, vol. 36, no. 11, pp. 2501-2511, Nov. 2003.
[36] P. Viola and M. Jones, “Rapid Object Detection using Boosted Cascade of Simple Features,” Computer Vision and Pattern Recognition, vol. 1, pp. 511-518, 2001.
[37] T. Hastie, R. Tibshirani, J. Friedman, “The Element of Statistcal Learning”, pp. 236-242, 299-302, 2001.
[38] P. Viola and M. Jones, “Robust Real-Time Object Detection,” IEEE ICCV Workshop Statistical and Computational Theories of Vision, July 2001.
[39] http://www.bioid.com/downloads/facedb/index.php
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔