臺灣博碩士論文加值系統

近年來，生物辨識被廣泛的融入到生活中。然而在人臉相似度的方法中，未曾看過Least Trimmed Square（LTS）Hausdorff Distance的研究。為此，本論文提出一個基於人臉影像相似度的比對系統。
本論文實驗中使用了UBKinFace Version 2、TwinsFace、VIPKinFace與DupeKorean等臉部影像做為實驗資料庫。並於影像比對系統中採用了Viola 與Jones 的臉部偵測方法，接著使用了動態形狀模型（Active Shape Model, ASM）快速地執行臉部特徵點定位，此方法有助於我們找到要計算的特徵點。藉由這些步驟，搭配尺度縮放與正規化處理技巧，人臉器官特徵點便可以自動地從臉部數位影像中萃取出來。接著將欲比對的兩張特徵點座標透過Hausodrff Distance 計算，並將計算出的Hausdorff Distance轉換為百分比。

Recently, biometrics has been widely used in our life. In the measurement of similar human face matching, there seems little study using the least trimmed square Hausdorff Distance (LTS-HD). In this paper, we use LTS-HD to measure facial similarity.
The face images come from the UBKinFace Version 2, TwinsFace, DupeKorean and VIPKinFace face databases. This system uses Viola and Jones’s face detection method. After face detection, the ASM (Active Shape Model) is applied for rapidly locating facial feature points such that the feature regions can be extracted accurately. Prior to ASM, there are preprocessing, scaling and normalizing. After ASM, facial component features can be extracted automatically from the digitized pictures of faces. The two coordinates of the feature points are compared by using Hausdorff Distance and converted to percentages.

誌謝 i
論文摘要 ii
Abstract iii
目錄 iv
圖目錄 vii
第一章 緒論 1
1.1 前言 1
1.2 研究動機 5
1.3 生物辨識技術發展與研究 5
1.3.1生物特徵辨識技術比較 5
1.3.2影像辨識裡的臉部偵測技術 8
1.3.3影像辨識裡的臉部特徵定位 9
1.4 研究目標 11
1.5 論文大綱 11
第二章 人臉辨識相關技術文獻回顧 12
2.1人臉辨識發展與研究 12
2.1.1人臉偵測定位技術 13
2.1.2人臉辨識與特徵萃取技術 18
2.2 Hausdorff Distance之發展與研究 23
2.2.1 Hausdorff Distance應用於人臉之相關文獻 23
2.2.2 Hausdorff Distance應用於非人臉之相關文獻 26
2.3相似臉之發展與研究 27
2.4人臉影像資料庫分析 30
第三章 相關影像處理技術 33
3.1彩色空間轉換 33
3.1.1 RGB彩色系統 33
3.1.2 HSV彩色系統 34
3.1.3 YCbCr彩色系統 36
3.2影像幾何 37
3.2.1 最近相鄰內插法（Nearest-Neighbor Interpolation） 37
3.2.2雙線性內插法（bilinear interpolation） 38
3.2.3雙立方內插法（Bicubic Interpolation） 38
第四章 相似人臉影像比對系統 39
4.1系統架構 39
4.2人臉萃取模組 39
4.2.1積分影像與Haar-like 特徵 40
4.2.2 AdaBoost 特徵分類器 41
4.2.3 Cascade 分類器 42
4.3器官特徵萃取模組 43
4.3.1 Active Shape Models訓練 43
4.3.2 Active Shape Models演算法 45
4.5相似度比對分析模組 46
4.5.1 Classic Hausdorff Distance 47
4.5.2 Partial Hausdorff Distance（PHD） 48
4.5.3 Modified Hausdorff Distance（MHD） 49
4.5.4 Least Trimmed Square （LTS） Hausdorff Distance 49
第五章 實驗與結果 50
5.1實驗環境 50
5.2人臉影像資料庫 50
5.3實驗結果分析與討論 52
5.3.1人臉萃取模組實驗結果 52
5.3.2器官特徵萃取模組實驗結果 55
5.3.3相似度比對分析模組實驗結果 56
第六章 結論 66
參考文獻 67

圖目錄
圖 1–1：生物辨識所使用的生理與行為特徵 2
圖 1–2：IBG生物辨識技術市場報告 2
圖 1–3：Restylane Imagine模擬玻尿酸注射劑的成效 4
圖 1–4：(a)指紋辨識機 (b)虹膜辨識機 7
圖 1–5：基本系統架構流程 11
圖 2–1：變形模板（deformable template）執行人臉偵測 14
圖 2–2：基於器官的人臉區塊 15
圖 2–3：類神經網路人臉偵測系統流程圖 17
圖 2–4：利用支持向量機分類人臉與非人臉 18
圖 2-5：Brunelli 和Poggio所使用的人臉樣板 21
圖 2-6：多分類器方法 (a)NN-HMM (b)HMM-NN 22
圖 2-7：Automated Face Recognition (AFR) system 24
圖 2-8：(a)輸入影像; (b)邊緣偵測後人臉影像; (c)經過 加權後之Edge map 24
圖 2-9：Dastmalchi等人提出ASPHD的像素比對示意圖 26
圖 2-10：人臉對齊系統 28
圖 2-11：第一列:Test Images第二列: Edge Densities 第三列: Picked Half-faces 30
圖 3-1：RGB色彩空間 33
圖 3-2：十二色色環 34
圖 3-3：明度呈現 35
圖 3-4：飽和度呈現 35
圖 3-5： HSV色彩空間 36
圖 3-7：雙線性內插法示意圖 38
圖 3-8：雙立方內插法示意圖 38
圖 4-1：相似人臉影像比對系統流程圖 39
圖 4-2：積分影像：（a）原始影像圖（b）左邊黃色區域存入（x, y） 40
圖 4-3：計算D 區域灰階值總和 40
圖 4-4：Haar-like 矩形特徵範例 41
圖 4-5：（a） T個重要的特徵取法 （b）AdaBoost 分類器架構 42
圖 4-6：Cascade加速機制示意圖 42
圖 4-7：Landmark特徵點取樣圖 44
圖 4-8：設好的特徵點所組成的灰階向量 45
圖 4-9：ASM定位人臉特徵點 46
圖 4-10：計算Hausdorff Distance圖形例子 48
圖 5-1：UB KinFace Database 的統計資料圖 51
圖 5-2：UB KinFace Database 51
圖 5-3：Twinsface Database 51
圖 5-4：VIPKinFace Database 52
圖 5-5：DupeKorean Database 52
圖 5-5：UB KinFace Database V2 正規化灰階影像 54
圖 5-6：UB KinFace Database V2 人臉偵測結果 54
圖 5-7：TwinsFace Database 正規化灰階影像 54
圖 5-8：TwinsFace Database人臉偵測結果 54
圖 5-9：VIPKinFace Database 正規化灰階影像 55
圖 5-10：VIPKinFace Database人臉偵測結果 55
圖 5-11：UBKinFace Database 器官特徵萃取結果 55
圖 5-12：TwinsFace Database 器官特徵萃取結果 55
圖 5-13：VIPKinface Database 器官特徵萃取結果 56
圖 5-14：UBKinFace非亞洲人中父母年輕與小孩最高相似度的母女影像 57
圖 5-15：UBKinFace亞洲人種中且父母年輕時與小孩最高相似度的父女影像 58
圖 5-16：非亞洲人種中且父母年長時與小孩最高相似度的父子影像 58
圖 5-17：亞洲人種中且父母年長時與小孩最高相似度的父子影像 58
圖 5-18：亞洲父母與小孩有較大相似度差距的父女影像 58
圖 5-19：亞洲父母與小孩有較大相似度差距的父子影像 59
圖 5-20：非亞洲父母與小孩有較大相似度差距的父女影像 59
圖 5-21：非亞洲父母與小孩有較大相似度差距的母女影像 59
圖 5-22：雙胞胎相似度影像 62
圖 5-23：VIPKinFace父子相似度影像 63
圖 5-24：DupeKorean資料庫最低相似度影像 64
圖 5-25：DupeKorean資料庫最高相似度影像 64

表目錄
表1-1：各種生物辨識方法優劣比較 8
表2-1：臉部資料庫 32
表5-1：實驗開發環境 50
表5-2：UB KinFace Database V2 無法偵測人臉的狀況 53
表5-3：四種HD的亞洲親屬相似度曲線圖 57
表5-4：四種HD的非亞洲親屬相似度曲線圖 57
表5-5：使用LTS-HD相同與不同家族人臉相似度分布圖 59
表5-6：亞洲人相似程度圖 60
表5-7：四種HD的雙胞胎相似度曲線圖 61
表5-8：雙胞胎相似程度 61
表5-9：相同與不同家族雙胞胎人臉相似度曲線圖 62
表5-10：四種HD的VIPKinFace相似度曲線圖 62
表5-11：相同與不同家族人臉相似度曲線圖 63
表5-12：交叉比對DupeKorean相似度結果 64
表5-13：整型前與整形後之DupeKorean相似度結果 65

[1] A. Samal and P. Iyengar, “Automatic Recognition and Analysis of Human Face and Facial Expressions: A Survey,” Pattern Recognition, vol. 25, pp. 65–77, 1992.
[2] I. Craw, H. Ellis, and J. Lishman, “Automatic Extraction of Face Features,” Pattern Recognition Letters, vol. 5, pp. 183–187,1987.
[3] V. Govindaraju, “Locating Human Faces in Photographs,” International Journal of Computer Vision 19,vol. 19, pp. 129–146,1996.
[4] V. Govindaraju, “A Computational Model for Face Location,” International Conference on Computer Vision, pp. 718–721 ,1990.
[5] J. G. Wang and T. N. Tan, “A New Face Detection Method Based on Shape Information,”Pattern Recognition Letters , vol. 21, pp. 463–471, 2000.
[6] M. C. Burl, T. K. Leung, P. Perona, “Face Localization via Shape Statistics,”In Proceedings International Workshop on Automatic Face and Gesture Recognition, pp.154–159 ,1995.
[7] C. Lu, C. Zhang, F. Wen, P. Yan, “Regional Feature Based Fast Human Face Detection,” Journal of TsingHua University,vol.39, pp. 101–105 ,1999.
[8] C. Xing, Y. Qu, S. Wang, “Face Detection on Gray-Scale Static Image with Complex Background,” Journal of Computer-Aided Design &Computer Graphics, vol. 14,no. 5, pp. 401–403 ,2002.
[9] H. He , “Face Location Based on Pixel Distribution Features in Grey Image,” Computer Engineer, vol. 28,no.6, pp. 158–160, 2002.
[10] C. H. Lee, J. S. Kim, and K. H. Park, “Automatic human face location in a complex background using motion and color information,” Pattern Recognition, vol. 29, no. 11, pp. 1877–1889, Nov. 1996.
[11] K. Schwerdt and J.L. Crowley, “Robust face tracking using color,” IEEE International Conference on Automatic Face and Gesture Recognition, 2000, pp. 90-95.
[12] 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.
[13] L. M. Tao, Z. Y. Peng, G. Y. Xu, “ The Features of Skin Color,” Journal of Software,vol.12,no7, pp.1032–1041,2001.
[14] H. Ai, L. Liang, G. Xu, B. Zhang, “Face Detection Base on Skin Color and Template,” Journal of Software,vol.12,no.12, pp .1784–1792,2001
[15] Z. Bian, X. Zhang,“Pattern Recognition,”Tsinghua University Press Second Edition,2000
[16] B. Moghaddam, A Pentland, “Probabilistic visual learning for object representation,” IEEE Transactions Pattern Analysis and Machine Intelligence, vol.19,No.7, pp. 696–710, 1997
[17] H. Zhu, L. Qu, H. J. Zhang, “Face Detection Based on Wavelet Transform and Support Vector Machine,”Journal of Xi'an Jiaotong University, vol.36, no.9, pp. 947–950, 2002
[18] K. Sung, T. Poggio, “Example-Based Learning For View-Based Human Face Detection.” IEEE Transactions Pattern Analysis and Machine Intelligence, vol.20, No.1, pp. 39–51 , 1998
[19] J. Zhu ,L. Q. Wang, G. Yuan, “Fast Face Location Based on Template Matching,” Computer Engineering, vol.28, no.9, pp.77–79, 2002
[20] A. Tsukamoto, C.-W. Lee, and S. Tsuji, “Detection and tracking of human face with synthesized templates,” International Conference on Computer Vision, pp. 183-186, 1993.
[21] L. H. Liang, H. H. Ai, K.H. He, “Multi-Template-Matching Based Single Face Detection,” Journal of Image and Graphics, vol. 4, no. 10, pp. 825–830, 1999.
[22] H. A. Rowley, S. Baluja, and T. Kanade, “Neural Network-Based Face Detection,” IEEE Transactions Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 23-38, Jan. 1998.
[23] P. Juell, R. Marsh, “A Hierarchical Neural Network for Human Face Detection.” Pattern Recognition, vol.29, no.5, pp.781–787, 1996.
[24] E. Osuna, R. Freund, F. Girosi, “Training Support Vector Machines: An Application to Face Detection,” In Proceedings Computer Vision and Pattern Recognition, pp.130–136, 1997.
[25] W. W. Bledsoe, “The Model Method in Facial Recognition,”Panoramic Research Inc, 1966.
[26] N. C. Kwang, D. J. Cross Andrew, R. H. Edwin, “Localising Facial Features with Matched Filters,” Audio and Video-Based Biometric Person Authentication Lecture Notes in Computer Science, vol.1206, pp.11–20, 1997.
[27] L. Y. Alan, “Deformable Templates for Face Recognition,” Cognitive Neuroscience, vol.3, no.1, pp.59–70, 1991.
[28] K. M. Lam and H. Yan,“Locating and Extracting the Eye in Human Face Images,” Pattern Recognition, Vol.29,No.5 , pp.771–779, 1996.
[29] K. Sobottka, L. Pitas. “A Fully Automatic approach to facial feature detection and tracking,” Audio- and Video-based Biometric Person Authentication Lecture Notes in Computer Science ,Vol.1206, pp. 77–84, 1997.
[30] H. S. Li, H. Yang, B. Y. Yuan, “Feature Extraction in Human Face Recognition System” Journal of Northern Jiaotong University, vol.25, no.2, pp. 18–21, 2001.
[31] X. Y. Zhao, “Locating Main Facial Features and Tracking Lip Moving Sequences,” Journal of Wuyi University , vol.16, no.1, 2002.
[32] M. Kirby , L. Sirovich,“Application of the Karhunen-Loeve Procedure for the Characterizeation of Human Faces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, no.1, pp. 103–108 ,1990.
[33] M. Turk and A. Pentland, “Eigenfaces for Recognition,” Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, 1991.
[34] P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 19, pp. 711-720, 1997.
[35] D. Swets and J. Weng, “Using Discriminant Eigenfeatures for Image Retrieval,” IEEE Transactions Pattern Analysis and Machine Intelligence, vol.18, no.8 , pp. 831–836, 1996.
[36] H. Peng, D. Zhang, “Dual Eigenspace Method for Human Face Recognition,”Electronics Letter, vol.33, no.4 , pp. 283–284 , 1997.
[37] M. Lades, J. Vorbuggen, J. Buhman, “Distortion Invariant Object Recognition in the
Dynamic Link Architecture,” IEEE Transactions on Computer, vol.42, no.3, pp. 300–311,1991.
[38] Ding Hong “Human Face Recognition Using Information of Face Components to Improve Elastic Matching,” Journal of optoelectorinics laser ,vol.13,No.8,2002.
[39] T. Kohonen “Associative Memory: A Dystem Theoretic Approach,”, Springer ,Berlin, pp.85–97 ,1997
[40] S. H. Lin, S. Y. Kung, and L. J. Lin, “Face Recognition/Detection by Probabilistic Decision-based Neural Network,”IEEE Transactions Neural Networks , vol.8, no.1, pp.114–132,1997
[41] S. Lawrence, C. L. Giles, A. C. Tsoi, “Face Recognition: a Convolutional Neural Network Approach,” IEEE Transactions on Neural Network, vol.8 , no.1, pp.98–113,1997.
[42] S. Y. Lee, Y. K. Ham, and R. H. Park, “Recognition of Human Front Faces Using Knowledge-based Feature Extraction and Neuro-fuzzy Algorithm,” Patten Recognition, vol.29, no.11, pp.1863–1876, 1996.
[43] Z Hong, “Algebraic Feature Extraction of Image for Recognition,” Patten Recognition, vol.24, no.3, pp.211–219,1991.
[44] X. W. Huang, J. Y. Yang, Y. F. Guo, “Human Facial Image Feature Extraction and Recognition Based on Degree of Membership” ACTA ELECTRONICA SINICA, vol.26 , no.5, pp.89–92, 1998.
[45] M. K. Hu, “Visual Pattern Recognition by Moment Invariant,” IRE Transactions on Information Theory, vol.8, pp.179–187, 1962.
[46] Gan Junying , Zhang Youwei “Face Recognition Based on Moment Invariants and Neural Networks,” Computer Engineering and applications, vol.38, no.7, pp.53–56,2002.
[47] Y. Zhu, L. C. De Silva, C. C. Ko, “Using Moment Invariants and HMM in Facial Expression Recognition,” Pattern Recognition Letters, vol.23, no.1–3, pp.83–91,2002.
[48] T. Phiasai, S. Arunrungrusmi, K. Chamnongthai. “Face Recognition System with PCA and Moment Invariant Method,” Proceedings of the IEEE International Symposium on Circuits and Systems, vol.2, pp.165–168,2001.
[49] R. Brunelli and T. Poggio. “Face Recognition:Features Versus Templates,” IEEE Transactions Pattern Analysis and Machine Intelligence, vol.15, no.10, pp.1042–1052, 1993.
[50] F. Samaria, S. Young, “HMM-based Architecture for Face Identification,” Image and Vision Computing, vol.12 , no.8, pp.537–543 , 1994.
[51] H. L. Jian, “Face Recognition Using Holistic Fourier Invariant Features.” Pattern Recognition, vol.34 , no.4, pp.95–109 , 2001.
[52] F. Goudail, “Face Recognition Dystem Using Local Autocorrelations and Multiscale Integration.” IEEE Transactions Pattern Analysis and Machine Intelligence, vol.18, no.10, pp. 1024–1028 , 1996.
[53] J. Benarie, D. Nandy, “A Volumetric/Iconic Frequency Domain Representation for Objects with Application for Pose Invariant Face Recognition.” IEEE Transactions Pattern Analysis and Machine Intelligence, vol.20 , no.5 , pp.449–457 ,1998.
[54] K. S. Yoon, Y. K. Ham, R. H. Park “Hybrid Approaches to Frontal View Face Recognition Using the Hidden Markov Model and Neural Network,”Pattern Recognition, vol. 31,no.3, pp.283–293, 1998.
[55] A. F. Abate, M. Nappi, D. Riccio, and G. Sabatino, “2D and 3D Face Recognition: A Survey,” Pattern Recognition Letters, vol.28, pp.1885–1906 ,2007.
[56] O. Jesorsky, K. J. Kirchbergand, and R. Frischholz, “Robust Face Detection Using the Hausdorff Distance,” In Proceedings Third International Conference on Audio and Video based Biometric Person Authentication, pp.90–95 , 2001.
[57] D. Huttenlocher, G. Klanderman, and W. Rucklidge, “ Comparing Images Using the Hausdorff Distance,” IEEE Transactions Pattern Analysis and Machine Intelligence, vol.15, no.9, pp.850–863, Sep. 1993.
[58] M. Dubuisson and A. K. Jain, “Modified Hausdorff Distance for Object Matching,”Proceedings 12th International Conference on Pattern Recognition, vol.1, pp.566–568,1994.
[59] B. Takacs, “Comparing Face Images Using the Modified Hausdorff Distance,”Pattern Recognition, vol.31, pp.1873–1881, 1998.
[60] Y. Gao and M. K. Leung, “Line Segment Hausdorff Distance on Face Matching,”Pattern Recognition, vol.35, no.2, pp.361–371,2002.
[61] Z. Y. Lin, “Face Component-based Gender Classification” Master Thesis, Department of Electrical Engineering, National Chi Nan University, Taiwan, R.O.C, 2010.
[62] B. F. Guo, K. M. Lam, K.H. Lin,W. C. Siu, “Human Face Recognition Based on Spatially Weighted Hausdorff Distance,” Pattern Recognition Letters, vol.24, issue: 1-3, pp. 499-507, January, 2003.
[63] K.H. Lin, K.M. Lam and W.C. Siu, “Spatially Eigen Weighted Hausdorff Distances for Human Face Recognition,”Pattern Recognition,vol.36,no.8, pp.1827–1834, 2003.
[64] Y. F. Cao “Biometrics Points Recorded Find the Password From Inside the Body” RUN!PC Information monthly issue, vol.188, pp.44–54, 2009.
[65] Q. F. Zhong,“Explore The Image Recognition in the Face Detection Technology”,CompoTech Component Technology, pp.84–87 , 2007.
[66] W. Rucklidge, “Locating Objects Using the Hausdorff Distance,” in Proceedings International Conference Computer Vision, pp. 457–464 ,1995.
[67] J. H. Kim,“Multiple Face Segmentation and Tracking Based on Robust Hausdorff Distance Matching,” Journal of Applied Science, pp.740–744. 2005.
[68] H. C. Tan, Y. J. Zhang, “A Novel Weighted Hausdorff Distance for Face Localization,” Image and Vision Computing, vol.24, pp.656–662 , 2006.
[69] H. Tan and Y. J. Zhang. “Computing Eigenface from Edge Images for Face Recognition based on Hausdorff Distance,” In Fourth International Conference on Image and Graphics, pp.639–644, Aug. 2007.
[70] X.M. Liu , Y.J. Zhang, H.C. Tan, “Hausdorff Distance Based Similarity Measure and Symmetry Measure and Their Applications in Face Localization.” Signal Processing, vol.24, no.1, 2008.
[71] H. Dastmalchi, J. Jafaryahya, R. Najafi, A. Daneshkhah “Averaged Segmental Partial Hausdorff Distance for Robust Face Recognition.” Proceedings of 2011 Second International Conference on Intelligent Systems, Modelling and Simulation, pp.35–39, 2011.
[72 ] P. Kalocsai, W. Zhao and E. Elagin. “Face similarity space as perceived by humans and artificial systems.” Proceedings 3rd IEEE International Conference on Automatic Face and Gesture Recognition, Japan, pp.177–180. 1998.
[73] B. Moghaddam, T. Jebara, A. Pentland, “Bayesian modeling of facial similarity,”Advances in Neural Information Processing Systems, vol. 11, pp. 910–916,1998.
[74] A. Holub, Y.-h. Liu, and P. Perona, “On Constructing Facial Similarity Maps,” Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
[75] C.L. Cheng, H. H. Xu, J. D. He, “Similar Face Image Retrieval by Local Feature ,” National Conference on Fuzzy Theory and its Applications, pp.256–261,2009.
[76] J. W. Lu, J. L. Hu, X. H. Zhou, Y. Y. Shang, Y. P. Tan, G. Wang, “Neighborhood Repulsed Metric Learning for Kinship Verification,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2594–2601, June 2012.
[77] V. Srisarkun, J. Cooper, “Face Recognition Using A Similarity-Based Measure In Image Database For Crime Investigation,” Video/Image Processing and Multimedia Communications 4th EURASIP-IEEE Region 8 International Symposium on VIPromCom , pp87–92 , 2002.
[78] S. Mozaffari and H. Behravan, “Twins Facial Similarity Impact on Conventional Face Recognition Systems,” In Proceedings of the 19th Iranian Conference on Electrical Engineering, 2011.
[79] N. Ramanathan, A.R. Chowdhury, and R. Chellappa, “Facial Similarity Across Age, Disguise, Illumination and Pose”,in Proceedings of International Conference on Image Processing, vol. 3, pp. 1999–2002, 2004.
[80] R.G. Fang, K. D. Tang, N. Snavely, T. H. Chen, “Towards Computational Models of Kinship Verification” in Proceedings of International Conference on Image Processing , pp. 1577–1580,2010.
[81]F. Li and M. K. H. Leung, “Two-stage Approach for Palmprint Identification Using Hough Transform and Hausdorff Distance,” in Proceedings of the IEEE International Conference on Control, Automation, Robotics and Vision, pp. 1–6, 2006.
[82] J. A. Gualtieri, J. LeMoigne, and C.V. Packer, “Distance Between Images,” Proceedings of Fourth Symp. Frontiers of Massively Parallel Computation, pp. 216–223, McLean, Va., Apr. 1992.
[83] J. You, E. Pissaloux, J. L. Hellec, P. Bonnin, “A Guided Image Matching Approach Using Hausdorff Distance with interesting points detection,” in Proceedings of International Conference on Image Processing ,vol.1, pp. 968 - 972 vol.1, 13–16 Nov. 1994.
[84] V. Di Gesù, V. Starovoitov, “Distance-based Functions for Image Comparison,”Pattern Recognition Letters, vol. 20, pp. 207–214, February, 1999.
[85] A. Ghafoor, R. N. Iqbal, and S. Khan, “Robust Image Matching Algorithm,” European Association for Signal Processing Conference on Video/Image Processing and Multimedia Communications, pp. 155–160, 2003.
[86] V. Perlibakas, “Distance Measures for PCA-based Face Recognition,” Pattern Recognition Letters, Volume: 25, pp. 711–724, 2004.
[87] R. Azencott, F. Durbin, and J. Paumard, “Multiscale identification of building in compressed large aerial scenes,” in Proceedings 13th International Conference Pattern Recognition, vol. 2, pp. 974-978, 1996.
[88] D. G. Sim, O. K. Kwon, R. H. Park, “Object Matching Algorithms Using Robust Hausdorff Distance Measures,” IEEE Transactions on Image Processing, vol.8, pp. 425–429, 1999.
[89] X. Yi; O. I. Camps, “Line-based Recognition Using a Multidimensional Hausdorff Distance,” IEEE Transactions Pattern Analysis and Machine Intelligence ,vol. 21, pp. 901–916, 1999.
[90] Z. Zhu, M. Tang, H. Lu, “A New Robust Circular Gabor Based Object Matching by
Using Weighted Hausdorff Distance,” Pattern Recognition Letters, vol. 25, pp. 515–523, 2004.
[91] O. K. Kwon, D. G. Sim, R.H. Park, “Robust Hausdorff Distance matching algorithms using pyramidal structures,” Pattern Recognition, vol.34, pp. 2005–2013, 2001.
[92] R. C. Gonzalez and R. E. Woods, Digital image processing, 2nd Edition, Prentice-Hall, New Jersey, 2002.
[93] K. Jensen and D. Anastassiou, “Subpixel edge localization and the interpolation of
still images,” IEEE Transactions on Image Processing, Vol. 4, pp. 285–295, 1995.
[94] M. Sonka, V. Hlavac, and R. Boyle, Image Porcessing, Analysis, and Machine Vision, PWS Pubishing, pp. 67–68, 1999.
[95]H. M. Liu ,“A Study of Image Interpolation Methods Based on Splines” , Master Thesis, The Department of Information & Computer Engineering, Chung-Yuan University, Taiwan, R.O.C, 2005.
[96] S.Y. Xia, M. Shao, J. B. Luo, and Y. Fu, “Understanding Kin Relationships in a Photo”, IEEE Transactions on Multimedia, Vol.14, pp.1046–1056, 2012.
[97] S.Y. Xia, M. Shao and Y. Fu, “Kinship Verification through Transfer Learning,” International Joint Conferences on Artificial Intelligence, pp. 2539–2544, 2011.
[98] M. Shao, S.Y. Xia and Yun Fu, “Genealogical Face Recognition Based on UB KinFace Database,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops , 2011.
[99] Y. Freund and R. E. Schapire, “A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting,” Journal of Computer and System Sciences, vol. 55, pp. 119–139, 1997.
[100]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.
[101] T. C. Hung “A Method for Face Recognition Based on Entropy and Hausdorff Distance” , Master Thesis, Department of Bioinformatics, Asia University, Taiwan, R.O.C, 2008.