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系統識別號 U0026-3107201515270300
論文名稱(中文) 以Gabor小波為基礎之使用分數指數項之多項式主成分分析於步態識別之研究
論文名稱(英文) Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Gait Recognition
校院名稱 成功大學
系所名稱(中) 電機工程學系碩士在職專班
系所名稱(英) Department of Electrical Engineering (on the job class)
學年度 103
學期 2
出版年 104
研究生(中文) 李豐旭
研究生(英文) Feng-Xu Li
學號 N27011058
學位類別 碩士
語文別 英文
論文頁數 49頁
口試委員 指導教授-李祖聖
口試委員-孔蕃鉅
口試委員-呂虹慶
口試委員-白能勝
口試委員-謝銘原
中文關鍵字 Gabor  小波轉換  步態辨識  核主成分分析 
英文關鍵字 Gabor  Wavelet transformation  Gait recognition  Kernel PCA 
學科別分類
中文摘要 本論文主要在探討在監視影片中,透過Gabor小波轉換取得影中人之步態特徵點,進而再透過使用分數形式的指數項多項式的核主成分分析來針對特徵點分類。因為人體步態的特徵點擷取大致上可分為時間及空間兩個層面,而本論文為了不失去影片中任何的資訊,因而採取時空兩者之資訊即影中人步行之剪影(silhouette)後,再針對剪影部分,以Gabor小波為基礎的迴積而取得該步態之特徵點,接著使用指數項為分數形式的多項式為核主成分分析來使前述的特徵點來進行分類的動作後,接著再以馬氏距離(Mahalanobis distance)來計算其相似度。最後進行模擬與實驗結果,可以展現以Gabor小波轉換為基礎之特徵點的確可以表現出較佳之辨識度。
英文摘要 In this thesis, we propose a method to extract the human gait features from the surveillance video through Gabor wavelet transformation, and then we classify these features by kernel principle component analysis (PCA) with the fractional power polynomial model. Because human gait feature extraction can be categorized into spatial and temporal domain, we will discuss the gait features in these two domains. In order not to lose any information from the surveillance video, this thesis uses the spatial-temporal silhouette of the people walking in the surveillance video, then we can have the gait features by taking silhouette convolution with Gabor based wavelet transformation. We classify these features by kernel PCA with the fractional power polynomial model. Finally, we use Mahalanobis distance to measure the similarity between the gait features. The simulation and the experiment results show that Gabor-based kernel PCA with fractional power polynomial models for Gait recognition have a better performance.
論文目次 Abstract(Chinese) I
Abstract(English) Ⅱ
Contents IV
List of Figures VI
List of Tables Ⅸ


Chapter 1. Introduction
1.1 Motivation 1
1.2 Thesis Organization 4

Chapter 2. Related Work
2.1 Introduction 5
2.2 Gait Features 6
2.3 Gait Recognition Approach 7
2.4 Summary 9

Chapter 3. Proposed Method
3.1 Introduction 10
3.2 Gabor Wavelets 11
3.3 The Augmented Gabor Feature Vector 13
3.4 Kernel PCA 16
3.5 Similarity Measures and Classification Rule 18
3.6 Summary 19
Chapter 4. Experiment results
4.1 Introduction 21
4.2 Recognition versus Different similarity measures 21
4.3 Recognition versus Different degrees of Polynomial kernels 22
4.4 The USF HumanID Gait Database 26
4.5 Experiment results 28
4.6 Summary 41

Chapter 5. Conclusions and Future Works
5.1 Conclusions 44
5.2 Future Works 45

References 46

參考文獻 [1] G. Johansson, “Visual perception of biological motion and a model for its analysis,” Perception and Psychophysics, vol. 14, pp.201–211, June1973.
[2] A. M. Bloch, “Stabilizability of nonholonomic control systems,” Automatica, vol. 28, no. 2, pp. 431-435, 1992.
[3] S. V. Stevenage, M. S. Nixon, and K. Vince. Visual analysis of gait as a cue to identity. Applied Cognitive Psychology, vol. 13, pp. 513–526, 1999.
[4] J.G. Daugman, “Two-Dimensional Spectral Analysis of Cortical Receptive Field Profile,” Vision Research, vol. 20, pp. 847-856, 1980.
[5] J.G. Daugman, “Uncertainty Relation for Resolution in Space,Spatial Frequency and Orientation Optimized by Two-Dimen-sional Visual Cortical Filters,” J. Optical Soc. Am., vol. 2, no. 7, pp. 1160-1169, 1985.
[6] M. Murray, A. Drought, and R. Kory, “Walking Pattern of Normal Men,” J. Bone and Joint Surgery, vol. 46-A, no. 2, pp. 335-360, 1964.
[7] G. Johansson, “Visual Motion Perception,” Scientific Am., vol. 232, pp. 76-88, 1975.
[8] A. Bobick and A. Johnson. Gait recognition using static activity-specific parameters. in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, volume 1, pp. 423–430, 2001.
[9] D. Cunado, M. Nixon, and J. Carter. “Automatic extraction anddescription of human gait models for recognition purposes,” Computer Vision and Image Understanding, vol. 90, no. 1, pp. 1–41, 2003.
[10] C. Yam, M. Nixon, and J. Carter. “Automated person recognition by walking and running via model-based approaches,” Pattern Recognition, vol. 37, no. 5, pp. 1057–1072, 2004.
[11] R. Urtasun and P. Fua, “3D tracking for gait characterization and recognition,” in Proc. of the 6th IEEE Int. Conf. on Automatic Face and Gesture Recognition, pp. 17–22, 2004.
[12] H. D. Yang and S. W. Lee, “Reconstruction of 3D human body pose for gait recognition,” in Proc. of the IAPR Conf. on Biometrics 2006, pp. 619–625, Jan. 2006.
[13] G. Ariyanto and M. Nixon. “Marionette mass-spring model for 3d gait biometrics,” in Proc. of the 5th IAPR Int. Conf. on Biometrics, pp. 354–359, March 2012.
[14] S. Sarkar, J. Phillips, Z. Liu, I. Vega, P. G. ther, and K. Bowyer. “The humanid gait challenge problem: Data sets, performance, and analysis,” Trans. of Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 162–177.Feb 2005.
[15] O. Mendoza, P. Melin, and G. Licea, “A hybrid approach for image recognition combining type-2 fuzzy, modular neural networks and the Sugeno integral,” Inf. Sci., vol. 179, no. 13, pp. 2078-2101, 2009.
[16] B. I. Choi and F. C. H. Rhee, “Interval type-2 fuzzy membership function generation methods for pattern recognition,” Inf. Sci., vol. 179, No. 13, pp. 2102-2122, 2009.
[17] R. Martinez, O. Castiollo, and L. T. Anguilar, “Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms,” Inf. Sci., Vol. 179, No. 13, pp. 2158-2174, 2009.
[18] T. C. Lin, H. L. Liu, and M. J. Kuo, “Direct adaptive interval type-2 fuzzy control of multivariable nonlinear systems,” ENG. Appl. Artif. Intell., Vol. 22, No. 3, pp. 420-430, 2009.
[19] J. Han and B. Bhanu. “Individual recognition using gait energy image,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 28, no.2, pp. 316–322, 2006.
[20] Z. Liu and S. Sarkar. “Simplest representation for gait recognition: Averaged silhouette”. in Proc. of the 17th Int. Conf. on Pattern Recognition, 2004 ICPR, pp. 211-214, 2004.
[21] Y. Makihara, R. Sagawa, Y. Mukaigawa, T. Echigo, and Y. Yagi, “Gait recognition using a view transformation model in the frequency domain,” in Proc. of the 9th European Conf. on Computer Vision, pp 151–163, Graz, Austria, May 2006.
[22] K. Bashir, T. Xiang, and S. Gong, “Gait recognition using gait entropy image,” in Proc. of the 3rd Int. Conf. on Imaging for Crime Detection and Prevention, pp. 1–6, Dec. 2009.
[23] T. H. W. Lam, K. H. Cheung, and J. N. K. Liu, “Gait flow image: A silhouette-based gait representation for human identification,” Pattern Recognition, vol. 44, no. 4, pp. 973–987, April 2011.
[24] K. Bashir, T. Xiang, and S. Gong, “Gait recognition without subject cooperation ,” Pattern Recognition Letters, pp. 2052–2060, Oct. 2010.
[25] D. Tao, X. Li, X. Wu, and S. Maybank, “Human carrying status in visual surveillance,” in Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 1670–1677, New York, USA, Jun. 2006.
[26] T. Lam and R. Lee. “A new representation for human gait recognition: Motion silhouettes image (msi),”in Proc. of the IAPR Int. Conf. on Biometrics, pp. 612–618, Jan. 2006.
[27] C. BenAbdelkader, R. Culter, H. Nanda, and L. Davis, “Eigengait: Motion-based recognition people using image self-similarity,” in Proc. of Int. Conf. on Audio and Video-based Person Authentication, pp. 284–294, 2001.
[28] S. Mowbray and M. Nixon. Automatic gait recognition via fourier descriptors of deformable objects. in Proc. of IEEE Conf. on Advanced Video and Signal Based Surveillance, pp. 566–573, 2003.
[29] Dacheng Tao, Xuelong Li, Xindong Wu Stephen J. Maybank “General Tensor Discriminant Analysis and Gabor Features for Gait Recognition” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 10, pp. 1700-1715, Oct. 2007.
[30] B. Scholkopf and A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond, MIT Press, 2002.
[31] B. Scholkopf, A. Smola, and K. Muller, “Nonlinear Component Analysis as a Kernel Eigenvalue Problem,” Neural Computation, vol. 10, pp. 1299-1319, 1998.
[32] B. Scholkopf and A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, 2002.
[33] Z. Liu and S. Sarkar, “Simplest Representation yet for Gait Recognition: Averaged Silhouette,” in Proc. IEEE Int’l Conf. Pattern Recognition, vol. 4, pp. 211-214, 2004.
[34] Y. Makihara, D. Muramatsu, H. Iwama, and Y. Yagi Osaka University, “On Combining Gait Features,” in Proc. 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1-8, 2013.
[35] M. Hu, Y. Wang, Z. Zhang, and Y. Wang, “Combining Spatial and Temporal Information for Gait based Gender Classification,” in Proc. of International Conference on Pattern Recognition, pp. 3679 - 3682, 2010.
[36] Z. Zhang, M. Hu, and Y. Wang, “A Survey of Advances in Biometric Gait Recognition,” Lecture Notes in Computer Science Volume 7098, 2011, pp 150-158
[37] M. Haghighat, S. Zonouz, and M. Abdel-Mottaleb, “Identification Using Encrypted Biometrics,” Computer Analysis of Images and Patterns, Springer Berlin Heidelberg, pp. 440-448, 2013.
[38] C. Liu, “Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 5, pp. 572 - 581, May 2004.
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