臺灣博碩士論文加值系統

在人臉偵測及人臉辨識的相關研究中，所需加強的不外乎準確率的提升，而輸入影像的前處理步驟及輸入影像的品質也將明顯影響後續處理的準確性。在本論文中，利用人臉特徵資訊來判斷人臉旋轉方向，以藉此找出連續影像中最接近正面的人臉影像。其中特徵抽取方法包括了膚色區域切割及邊緣資訊的結合，根據所抽取之人臉特徵影像，提出了三種方法分別解決了人臉三方向的旋轉問題。
　　第一種方法利用特徵垂直集中性，判斷人臉特徵左右集中位置，以得到人臉左右轉動角度及方向。第二種方法使用放射型樣板統計人臉特徵影像中每個區域的特徵點數，將所得到的放射型直方圖加以分析及根據人臉分佈特性，可以判斷出人臉正面旋轉角度。第三種方法藉由頸部位置與人臉區域及特徵區域的相對位置關係，判斷其俯仰的可能性。
　　利用上述三種方法的旋轉偵測，配合人眼位置計算及對稱度的比較，以計算出最接近正面的人臉影像，經由自行拍攝的影像測試結果顯示，我們所提出的方法在人臉正面判定上，的確具有可行性及其正確。

It is an urgent desire for researchers to uplift the accuracy in human face detection and recognition. The main bottleneck lies mainly on the quality of input images which definitely drastically affect the accuracy of the system. In this thesis, the information of facial feature is adopted to determine the direction of face rotation and then find the best shot of faces in a surveillance video stream so that the quality of input images can be improved. The features to be extracted include skin color information and edge information. Skin color information can be obtained by analyzing the skin color distribution in YCbCr color space and edge information can be detected by applying Sobel edge detector. Moreover, three strategies are proposed to determine the three directions of face rotation based on the feature image.
The first strategy uses the collection of vertical feature projection. The direction and the angle of face turning can be determined by analyzing the distribution of vertical projection histogram. The second strategy uses a novel model called radial template to detect the presence of face rotation. This template is designed to find the angle of center-rotated objects. According to the characteristics of skin detection and edge extraction, the extracted feature will be stable under this kind of template. The third strategy is to determine the presence of bending or lifting of faces based on the relationship of feature area and neck position.
By integrating the three strategies and the geometry of eye position and judgment of symmetry, the best shot of frontal face image can be identified which can be employed in later face recognition task.
Experiments were conducted on various video images containing faces. Experimental results verify the feasibility and validity of our proposed approach in determining the most frontal faces in video sequences.

第一章 緒論 1
1.1研究動機 1
1.2相關研究 3
1.3系統架構 5
1.4論文架構 7
第二章 頭部偵測Head Detection 8
2.1移動物偵測 9
2.1.1前景物判斷 10
2.1.2前景物處理 11
2.2頭部判斷-利用前景物形狀 14
2.3膚色區域偵測 16
2.3.1 膚色區域偵測 16
2.4頭部偵測-結合膚色範圍 20
第三章 旋轉判斷 23
3.1 轉動判斷 23
3.1.1　特徵集中性與垂直投影 23
3.1.2 影像處理過程 26
3.1.2轉動偵測實驗結果 28
3.2 平面旋轉 30
3.2.1放射狀樣板 30
3.2.2臉部特徵 32
3.2.3樣板比對 33
3.2.4平面旋轉實驗結果 36

3.3 仰俯偵測： 37
第四章 可辨識度預測 40
4.1 旋轉計算 40
4.2 眼睛偵測 43
4.3 對稱度比較 45
4.3.1結構性對稱 45
4.3.2亮度對稱 46
4.4 分數統計計算 48
第五章 實驗結果與討論 50
5.1實驗結果 51
5.1.1前景物偵測及頭部偵測 51
5.1.2旋轉判斷 53
5.1.3眼睛偵測及對稱度計算 56
5.1.4正面預測 58
5.2實驗結果討論 62
第六章 結論及未來方向 63
6.1結論 63
6.2未來研究 65
參考文獻 66

[1] Chiunhsiun Lin, Kuo-Chin Fan “Triangle-based approach to the
　detection of human face” Pattern Recognition Vol.34 , pp.1271-1284 ,
2001.
[2] J.Miao , B.Yin , K.Wang , L.Shen , X.Chen” A Hierarchical Multiscale
and Multiangle System for Human Face Detection in a complex
Background Using Gravity-Center Template “ Pattern Recognition
vol.32 , No.7 , pp.1237-48 , 1999
[3] Y.Ishii, H.Hongo, K.Yamamoto, Y.Niwa “Real-Time Face and Head
Detection using Four Directional Features” Automatic Face and
Gesture Recognition, Sixth IEEE International Conference 17-19
pp.403 - 408 , 2004.
[4] F.Marquks, V.Vilaplana, A.Buxes “Human Face Segmentation And
Tracking Using connected Operators and Partition Projection” Image
Processing Vol.3 ,24-28 , pp.617 - 621, 1999.
[5] K.H.Seo , W.Kim , C.Oh , J-J.Lee “Face Detection and Facial Feature
Extraction Using Color Snake” Industrial Electronics , IEEE
International Symposium Vol.2 ,8-11 , pp.457 – 462 , 2002.
[6] L.Zhi-fang1 , Y.Zhi-sheng1 , A.K.Jain2 , W.Yun-qiong “Face
Detection And Facial Feature Extraction In Color Image”
Computational Intelligence and Multimedia Applications 27-30 ,
pp.126 - 130 , 2003.
[7] S.K.Singh , D.S.Chauhan , M.Vatsa , R.Singh “A Robust Skin Color
Based Face Detection Algorithm” Tamkang Journal of Science and
Engineering , Vol. 6 , No. 4 , pp.227-234 , 2003.
[8] C.C.Han , H.Y.M.Liao , G.J.Yu , L.H.Chen “Fast face detection via
morphology-based pre-processing” Pattern Recognition Vol.33 ,
pp.1701-1712 , 2000.
[9] C.Garcia , M.Delakis “Convolutional Face Finder : A Neural
Architecture for Fast and Robust Face Detection” IEEE
Transactions on pattern analysis and machine intelligence , Vol.26,
No.11 , 2004.
[10] X.guang , L.J.Zhou , C.S. Zhang” A Novel Algorithm for Rotated
Human Face Detection” Computer Vision and Pattern Recognition vol.1 , 13-15 , pp.760 - 765 , 2000.
[11] H.Liu , S.Yon, X.Chen, W.Gao “Rotated Face Detection in Color
Image Using Radial Template(RT)” Acoustics, Speech, and Signal
Processing IEEE International Conference Vol.3 , 6-10
pp.III - 213-16 , 2003.
[12] Z.YANG, H.AI, B.WU, S.LAO and L.CAI “Face Pose Estimation and
its Application in Video Shot Seletion” Proceeding of the 17th
International Conference on Pattern Recognition , 2004.
[13] P.Smith , M.Shah, N.d.V.Lobo, “Determining Driver Visual Attention
With One Camera” Intelligent Transportation Systems, IEEE
Transactions Vol.4 , Issue 4, pp.205 – 218 , 2003.
[14] S.Han, G.Pan , Z.Wu “Human Face Orientation Detection Using
Power Spectrum Based Measurements” Automatic Face and Gesture
Recognition, Sixth IEEE International Conference 17-19pp.791 - 796 , 2004.
[15] W.Cao , H.Feng , G.Xiao, Y.Gu, S.Wang ”Study of An Algorithm
For Face Pose Adjustment Based On Eye Location” Intelligent Control
and Automatio Vol.5 , 15-19 pp.4190 - 4194 , 2004.
[16] B. Yegnanarayana, A.K.sao , B. V. K. Vijaya Kumar, M.Savvides
“Determination of pose angle of face using dynamic space warping”
Information Technology: Coding and Computing Vol.1 , 5-7
pp.661 - 664 , 2004.
[17] C. Garcia, and G.Tziritas. “ Face Detection Using Quantized Skin
Color Regions Merging and Wavelet Packet Analysis.” in IEEE
Transactions on Multimedia vol. 1 , No. 3 , pp. 264-277, 1999.
[18]K. K. Sung , T. Poggio, “Example-based learning for view-based
human face detection”, in Proc. Image Understanding Workshop, pp.
843-850 , 1994.
[19]H. A. Rowley, S. Baluja, and T. Kanade, “Human face detection in
visual scenes”, Tech. Rep. CMU-CS-95-158R, Carnegie Mellon
University, 1995.
[20]S. H. Jeng, H. Y. Mark Liao, C. C. Han, M. Y. Chern, and Y. T. Liu,
“An efficient approach for facial feature detection using geometrical
face model”, Pattern Recognition, 1997.
[21]B. Moghaddam, and A. Pentland, “Probabilistic Visual Learning for
Object Representation,” IEEE Trans. Pattern Analysis and Machine
Intelligence, Vol. 19, No. 7, pp. 696-710, 1997.
[22]B. Moghaddam, W. Wahid, and A. Pentland, “Beyond Eigenfaces:
Probabilistic Matching for Face Recognition,” IEEE International
Conference on Automatic Face and Gesture Recognition, pp. 21-35,
1998.
[23]E. Osuna, R. Freund, and F. Girosi, “Training Support Vector
Machines : an Application to Face Detection,” Proc. Computer Vision
and Pattern Recognition, pp. 17-19, 1997.