基於小波與支持向量機演算法之人臉偵測研究 陳南樺、黃登淵
E-mail: 9606959@mail.dyu.edu.tw
摘 要
成功的人臉偵測是後續人臉辨識的重要基礎，因此準確之人臉偵測技術在人臉辨識中扮演著相當重要的角色。本文首先提 出基於知識與特徵之人臉偵測演算法，以解決橢圓標定誤判之問題。其次，基於支持向量機之人臉偵測演算法亦被提出，
以用來解決因亮度不足造成膚色分割不完整之問題。 基於知識與特徵之人臉偵測演算法，主要是利用人臉特徵以進行臉部 擷取與定位。本文所提出之橢圓標定及三角標定均能快速且正確的標定人臉，其中利用眼睛與嘴巴所構成之幾何特徵三角 標定，更能降低橢圓標定易產生誤判之機率。 至於基於支持向量機之人臉偵測演算法，其方法是透過大量樣本之學習訓練
，以達到人臉偵測的目的。本文首先採用支持向量學習機器(Support Vector Learning Machines)訓練產生多重色彩空間膚色 模型(multi-color space skin color model)，再配合區域方塊閥值設定，可有效解決因亮度不足、背光與陰影下膚色分割不完整 之問題。當膚色被順利分割出來後，人臉特徵可採用預先以SVM訓練完成之灰階臉模型來加以框取。因此透過膚色分割與 灰階臉搜尋規則，便能降低灰階臉框架比對之次數，但誤判之機率仍需進一步降低。
關鍵詞 : 人臉偵測 ; 膚色分割 ; 支持向量機
目錄
封面內頁 簽名頁 授權書... iii 中文摘要... iv 英文摘要... v 誌謝... vi 目錄... vii 圖目錄... x 第一章 緒論 1.1 前 言... 1 1.2 研究目的... 2 1.3 人臉偵測之研究方法... 3 1.4 本文架 構... 6 第二章 支持向量機(SVM) 2.1 前言... 8 2.2 線性可分離...
10 2.3 線性不可分離... 13 2.4 非線性可分離... 16 第三章 人臉偵測系統之數位影像處理相 關技 3.1 色彩模型與膚色的關係... 20 3.1.1 RGB色彩模型... 20 3.1.2 YCbCr色彩模
型... 22 3.1.3 YIQ色彩模型... 24 3.1.4 XYZ色彩模型... 25 3.1.5 其他色彩模 型... 27 3.1.5.1 正規化RGB色彩模型... 27 3.1.5.2 HSV色彩模型... 28 3.1.5.3 HSI色 彩模型... 29 3.1.5.4 HSL色彩模型... 30 3.2 彩色影像的分割... 31 3.2.1 膚色分 析... 31 3.2.2 影像二值化... 32 3.3 二值影像的應用... 34 3.3.1 影像形態 學... 34 3.3.2 二值影像之連通區域... 36 3.4 小波(Haar)轉換之應用... 38 3.4.1 小波轉 換理論簡述... 39 第四章 橢圓區域與三角特徵人臉偵測系統流程與實驗結果 4.1 橢圓區域與三角特徵人臉偵 測系統流程... 41 4.1.1 固定色彩閥值之膚色分析... 42 4.1.2 影像二值化與二值影像之應用... 44 4.2 橢圓 區域人臉影像標定... 48 4.3 三角特徵人臉影像標定... 50 4.4 實驗結果... 51 4.5 實 驗討論... 53 第五章 支持向量機人臉偵測系統流程與實驗結果 5.1 支持向量機人臉偵測系統流
程... 60 5.1.1 膚色訓練模型選取機制... 60 5.1.1.1 排除暗紅色系之膚色訓練模型... 61 5.1.1.2 解決亮 度不足之膚色訓練模型... 62 5.2 支持向量機膚色的分類... 63 5.3 支持向量機灰階臉的分類... 67 5.4 實驗結果討論... 71 5.4.1實驗結果... 71 5.4.2實驗討論... 77 第六章 結論與未來研究方向 6.1結論... 78 6.2未來研究方向... 79 參考文
獻... 80 參考文獻
[1] M. H. Yang,” Detecting Faces in Images: A Survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 24, no.
1, pp. 34-58,2002.
[2] Yang, G. and T. S. Huang Human face detection in complex background. Pattern Recognition, 27(1),53-63. 1994 [3] Hsu, R. L., M.
Abdel-Mottaleb and A. K. Jain Face detection in color images, IEEE Trans. Pattern Analysis and Machine Intelligence, 24(5), 696-706. 2002 [4]
Sandeep, K. and A. N. Rajagopalan Human Face Detection in Cluttered Color Image Using Skin Color and Edge Information, Indian Conference on Computer Vision, Graphics and Image Processing, Ahmedabad, India. 2002 [5] 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, International Conference on Pattern Recognition, Brisbane, Australia, 1350-1355. 1998 [6] Yullie, P. Hallinan and D. Cohen Feature exaction from faces using deformable
templates. International Journal of Computing Vision, 8(2), 99-111. 1992 [7] M. Soriano, B. Martinkauppi, S. Huovinen, and M. Laaksonen,
“Skin color modeling under varying illumination conditions using the skin locus for selecting training pixels,” Proceedings of IEEE Nordic Signal Processing Symposium, pp. 383-386, 2000.
[8] M. Soriano, M. Martinkauppi, S. Huovinen, and M. Laaksonen, “Skin detection in video under changing illumination conditions,”
Proceedings of the 15th International Conference on Pattern Recognition, Vol. 1, pp. 839-842, 3-7 Sept. 2000.
[9] A. Hadid, M. Pietikainen, and B. Martinkauppi, “Color-based face detection using skin locus model and hierarchical filtering,” Proceedings of the 16th International Conference on Pattern Recognition, Vol. 4, pp. 196-200, 2002.
[10] Yullie, P. Hallinan and D. Cohen Feature exaction from faces using deformable templates. International Journal of Computing Vision, 8(2), 99-111. 1992 [11] Jain, K., Y. Zhong and M-P Jolly Deformable template models: A review. Signal Processing, 71(2), 109-129. 1998 [12] Bruneli, R. and T. Poggio Face recognition: Features versus templates, IEEE Transactions on pattern analysis and machine intelligence, 15(10), 1042-1052.
1993 [13] Turk, M. and A. Pentland Eigenfaces for recognition. Journal of cognitive neuroscience, 3(1), 71-86. (1991) [14] Rowley, H. A., S. Baluja and T. Kanade Neural Network-Based Face Detection, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, California, 203-208. (1996) [15] H. A. Rowley, S. Baluja and T. Kanade, ”Rotation Invariant Neural Network-Based Face Detection,
” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Santa, Barbara, California, 38-44, 1998.
[16] M. Turk, and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86, 1991.
[17] S. Y. Lee, Y. K. Ham, and R. H. Park, “Recognition of human front faces using knowledge-based feature extraction and neuro-fuzzy algorithm,” Pattern Recognition, Vol.29, No.11, pp. 1863-1867, 1996.
[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] Oren, M. Papageorgiou, C. Sinha, P. Osuna, E. Poggio, T. “Pedestrian Detection Using Wavelet Templates” Proceedings of 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 17-19 June, pp.193-199, 1997 [20] P. Papageorgiou and M. Oren,”
A General Framework for Object Detection ” Center for Biological and Computational Learning Artificial Intelligence Laboratory.
[21] Constantine. Papageorgiou, Tomaso. Poggio, “A Trainable System for Object Detection” International Journal of Computer Vision 38(1), 15–33, 2000 [22] Elena. Casiraghi, Raffaella. Lanzarotti, Giuseppe. Lipori, “A face detection system based on color and support vector machines” Dipartimento di Scienze dell’Informazione Universit`a degli Studi di Milano Via Comelico, 39/41 20135 Milano, Italy [23] Junwei Han, George M. Award, Alistair Sutherland, and Hai Wu, Automatic Skin Segmentation for Gesture Recognition Combining Region and Support Vector Machine Active Learning* School of Computing, Dublin City University, Ireland [24] Haizhou ,Ai. Luhong, Liang. and Guangyou, Xu.
Face detection based on template matching and support vector machines. IEEE International Conference on Image Processing, Volume 1, pp.1006 - 1009, 7-10 Oct, 2001 [25] C.C. Chang, C.J.Lin, “LIBSVM:A Library for Support Vector Machine”,
http//www.csie.ntu.edu.tw/~cjlin/libsvm/index.html, 2003 [26] V. Vapnik , S. Golowich , and A. Smola,“ Support Vector method for function approximation，regression estimation，and signal processing ,” In M. Mozer ， M. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pages 281-287, Cambridge, MA，1997.MIT Press.
[27] 林群雄,“基於三角幾何學及顏色特徵作人臉偵測、人臉角度分類與人臉辨識”,中華民國九十年六月十八日, 國立中央大學資訊工程 研究所博士論文。
[28] 張鴻豪,“使用支持向量委員會機器於適應性色彩空間切換之臉部追蹤”,中華民國九十五年六月,國立雲林科技大學電子工程研究所 碩士論文。
