簡易檢索 / 詳目顯示

研究生: 林家緯
Lin, Jia-Wei
論文名稱: 使用Retinex演算法作光線變化下之人臉辨識
Face Recognition under Illumination Variation by Using Retinex Algorithm
指導教授: 賴源泰
Lai, Yen-Tai
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年: 2014
畢業學年度: 102
語文別: 英文
論文頁數: 60
中文關鍵詞: 人臉辨識Retinex演算法區域二位元描述
外文關鍵詞: face recognition, local binary patterns, linear discrimination analysis
相關次數: 點閱:85下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 目前人臉辨識已經是非常熱門的研究主題,因為可以廣泛地應用在許多領域當中,例如:資訊安全、警監系統、人機互動、犯罪人員辨識。
    人臉辨識的準確率容易受到光線變化,或是人臉表情、頭部姿勢改變等影響。本篇論文提出一個架構,將人臉影像以Retinex 演算法進行陰影去除後,再以區域二位元編碼方式描述特徵,整體和局部特徵擷取的架構解決了因為光線變化而導致辨識率下降的問題。
    由實驗結果得知,在有限的訓練影像下,經由陰影去除以及使用整體和區域特徵擷取提高了人臉辨識系統的辨識率。

    Face recognition has been an active research area due to its wide range of application in information security, video surveillance systems, human-computer interaction, and criminal verification.
    Illumination variation, facial expression, and pose variation remain a persistent challenge in face recognition. In the thesis, we proposed a face recognition system which can resolve the problem caused by illumination variation. In our method, Retinex algorithm is adopted to remove the shadow of face firstly, and Local binary pattern is used to describe face feature. We propose global and local discriminative features for face recognition under various facial conditions based on robust feature description and feature extraction.
    Experimental results demonstrate that our method can improve face recognition rate when amount of training images limited.

    Chapter 1 Introduction 1 1.1 Motivation 1 1.2 literature Survey 2 1.3 Organization of The Thesis 6 Chapter 2 Face Recognition System Overview 7 2.1 Image Normalization 7 2.1.1 Retinex Algorithm 7 2.1.2 Single-Scale Retinex 8 2.1.3 Multi-Scale Retinex 9 2.2 Feature Description 11 2.2.1 Histogram Equalization 11 2.2.2 Local Binary Patterns 13 2.2.3 Local Ternary Patterns 14 2.3 Feature Extraction 16 2.3.1 Principal Component Analysis 16 2.3.2 Linear Discriminant Analysis 20 2.3.3 Locality Preserving Projections 24 2.4 Classifier 27 2.4.1 k-Nearest Neighbor 27 2.4.2 Support Vector Machine 28 Chapter 3 Proposed Methods 33 3.1 Proposed Methods Flowchart 33 3.2 Image Normalization 34 3.3 Global and Local Discriminative Feature 35 3.4 Classifier 37 3.5 Global and Local Discriminative Feature Framework 38 3.6 Training Process of Face Recognition 39 3.7 Testing Process of Face Recognition 41 Chapter 4 Experimental Results 43 4.1 Face Image Database 43 4.2 Experience Results 43 4.2.1 Results by using Retinex Algorithm 44 4.2.2 Results of Combination Classifier 47 Chapter 5 Conclusions 55 References 56

    [1] W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: A literature survey,” Acm Computing Surveys (CSUR), vol. 35, pp. 399-458, 2003.
    [2] P.N. Belhumeur and D.J. Kriegman, “What is the set of images of an object under all possible lighting conditions?” IEEE Int. Conference on Computer Vision and Pattern Recognition, pp. 52 - 58, 1997.
    [3] T.R. Raviv and A. Shashua, “The quotient image: Class based re-rendering and recognition with varying illuminations,” IEEE Int. Conference on Computer Vision and Pattern Recognition, pp. 566 - 571, 1999.
    [4] H.T. Wang, S.Z. Li, Y.S. Wang, “Face Recognition under Varying Lighting Conditions Using Self Quotient Image,” Sixth IEEE International Conference on Automatic Face and Gesture Recognition. Proceedings,
    pp. 17-19, 2004.
    [5] Yung-Mao Lu, Bin-Yih Liao, Jeng-Shyang Pan, “Face Recognition Algorithm Decreasing the Effect of Illumination,” Int. Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2008
    [6] C.P. Chen and C.S. Chen, “Lighting Normalization with Generic Intrinsic Illumination Subspace for Face Recognition,” IEEE International Conference on Computer Vision, 2005.
    [7] Moonhwi Lee, Cheong Hee Park, “An Efficient Image Normalization Method for Face Recognition Under Varying Illuminations,” IEEE Trans. on Pattern Analysis and Machine Intelligence, pp. 711 - 720, 1997.
    [8] Jobson, D.J., Rahman, Z., Woodell, G.A. “Properties and Performance of a center/surround Retinex.” IEEE Transactions on Image Processing , vol. 3, pp.451–462 ,1997.
    [9] Jobson, D.J., Rahman, Z., Woodell, G.A. “A Multiscale Retinex for Bridging the Gap Between Color Images and the Human Observations of Scenes.” IEEE Transactions on Image Processing, 6(7), 897–1056 , 1997.
    [10] T. Ahonen, A. Hadid, and M. Pietikainen, “Face description with local binary patterns: Application to face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 2037-2041, 2006.
    [11] X. Tan and B. Triggs, “Enhanced local texture feature sets for face recognition under difficult lighting conditions,” IEEE Transactions on Image Processing, vol. 19, pp. 1635-1650, 2010.
    [12] J. H. Lai, P. C. Yuen, and G. C. Feng, “Face recognition using holistic Fourier invariant features,” Pattern Recognition, vol. 34, pp. 95-109, 2001.
    [13] W. Hwang, G. Park, J. Lee, and S. C. Kee, “Multiple face model of hybrid fourier feature for large face image set,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1574-1581, 2006.
    [14] Y. Su, S. Shan, X. Chen, and W. Gao, “Hierarchical ensemble of global and local classifiers for face recognition,” IEEE Transactions on Image Processing, vol. 18, pp. 1885-1896, 2009.
    [15] S. I. Choi and G. M. Jeong, “Shadow Compensation Using Fourier Analysis With Application to Face Recognition,” IEEE Signal Processing Letters, vol. 18, pp. 23-26, 2011.
    [16] M. Savvides, J. Heo, R. Abiantun, C. Xie, and B. V. K. V. Kumar, “Class dependent kernel discrete cosine transform features for enhanced holistic face recognition in FRGC-II,” in IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 185-188, 2006.
    [17] Z. M. Hafed and M. D. Levine, “Face recognition using the discrete cosine transform,” International Journal of Computer Vision, vol. 43, pp. 167-188, 2001.
    [18] W. Chen, M. J. Er, and S. Wu, “Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 36, pp. 458-466, 2006.
    [19] J. T. Chien and C. C. Wu, “Discriminant waveletfaces and nearest feature classifiers for face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, pp. 1644-1649, 2002.
    [20] J. Ravi and S. S. Tevaramani, “Face Recognition using DT-CWT and LBP Features,” 2012 International Conference on Computing, Communication and Applications (ICCCA), pp. 6, 2012.
    [21] J. G. Daugman, “Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters,” Optical Society of America, Journal, A: Optics and Image Science, vol. 2, pp. 1160-1169, 1985.
    [22] S. Liao, X. Zhu, Z. Lei, L. Zhang, and S. Li, “Learning multi-scale block local binary patterns for face recognition,” Advances in Biometrics, pp. 828-837, 2007.
    [23] L. Wolf, T. Hassner, and Y. Taigman, “Descriptor based methods in the wild,” Faces in Real-Life Images Workshop in ECCV, pp.4-12, 2008.
    [24] M. Heikkilä, M. Pietikäinen, and C. Schmid, “Description of interest regions with local binary patterns,” Pattern Recognition, vol. 42, pp. 425-436, 2009.
    [25] A. Hafiane, G. Seetharaman, and B. Zavidovique, “Median binary pattern for textures classification,” Image Analysis and Recognition, pp. 387-398, 2007.
    [26] F. Ahmed, E. Hossain, A. Bari, and A. Shihavuddin, “Compound local binary pattern (CLBP) for robust facial expression recognition,” in IEEE 12th International Symposium on Computational Intelligence and Informatics, pp. 391-395, 2011.
    [27] W. Yang and C. Sun, “Face recognition using improved local texture patterns,” in 2011 9th World Congress on Intelligent Control and Automation, pp. 48-51, 2011.
    [28] T. Jabid, M. H. Kabir, and O. Chae, “Local Directional Pattern (LDP)–A Robust Image Descriptor for Object Recognition,” in 2010 Seventh IEEE International Conference on Advanced Video and Signal Based Surveillance , pp. 482-487, 2010.
    [29] W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang, “Local Gabor binary pattern histogram sequence (LGBPHS): A novel non-statistical model for face representation and recognition,” vol. 1, pp. 786-791 ,2005.
    [30] S. Xie, S. Shan, X. Chen, and J. Chen, “Fusing local patterns of gabor magnitude and phase for face recognition,” IEEE Transactions on Image Processing, vol. 19, pp. 1349-1361, 2010.
    [31] M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of cognitive neuroscience, vol. 3, pp. 71-86, 1991.
    [32] 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.
    [33] M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent component analysis,” IEEE Transactions on Neural Networks, vol. 13, pp. 1450-1464, 2002.
    [34] S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science, vol. 290, pp. 2323-2326, 2000.
    [35] X. Niyogi, “Locality preserving projections,” p. 153, 2004.
    [36] X. He, S. Yan, Y. Hu, P. Niyogi, and H. J. Zhang, “Face recognition using laplacianfaces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 328-340, 2005.
    [37] T. Zhang, B. Fang, Y. Y. Tang, Z. Shang, and B. Xu, “Generalized discriminant analysis: A matrix exponential approach,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 40, pp. 186-197, 2010.
    [38] Q. Tian, M. Barbero, Z. H. Gu, and S. H. Lee, “Image classification by the Foley-Sammon transform,” Optical Engineering, vol. 25, pp. 834-840, 1986.
    [39] D. Q. Dai and P. C. Yuen, “Face recognition by regularized discriminant analysis,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 37, pp. 1080-1085, 2007.
    [40] H. Zhao and P. C. Yuen, “Incremental linear discriminant analysis for face recognition,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 38, pp. 210-221, 2008.
    [41] H. Xiong, M. Swamy, and M. Ahmad, “Two-dimensional FLD for face recognition,” Pattern Recognition, vol. 38, pp. 1121-1124, 2005.
    [42] P-N. Tan, M. Steinbach, V. Kumar, Introduction to Data Mining, Addison-Wesley Publishing, 2006.
    [43] M. Kantardzic, Data Mining: Concepts, Models, Methods, and Algorithms, John Wiley & Sons Publishing, 2003.
    [44] I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Publishing, 2005.
    [45] Y.S. Kim, “Comparision of the decision tree, artificial neural network, and linear regression methods based on the number and types of independent variables and sample size,” Journal of Expert Systems with Application, Elsevier, pp. 1227-1234, 2008.
    [46] E. Land , “An alternative technique for the computation of the designator in the retinex theory of color vision” Proc. Natl. Acad. Sci. U. S. A. 83:3078-80.
    [47] R. Gross, V. Brajovic, “An image preprocessing algorithm for illumination invariant face recognition,” Fourth International Conference on Audio and Video Based Biometric Person Authentication, pp. 10–18, 2003.
    [48] K. Barnard, G. Finlayson, and B. Funt, “Color constancy for scenes with varying illumination,” 4th European Conference on Computer Vision, pp., Bernard Buxton and Roberto Cipolla, eds, Springer, 1996.
    [49] T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24,
    pp. 971-987, 2002.
    [50] M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586-591, 1991.
    [51] R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Annals of Human Genetics, vol. 7, pp. 179-188, 1936.
    [52] Aizerman, Mark A.; Braverman, Emmanuel M.; and Rozonoer, Lev I.. “Theoretical foundations of the potential function method in pattern recognition learning.” Automation and Remote Control , pp.821-837, 1964.
    [53] Boser, B. E.; Guyon, I. M.; Vapnik, V. N.. “A training algorithm for optimal margin classifiers.” Proceedings of the fifth annual workshop on Computational learning theory ,vol. 2, pp. 144, 1992.
    [54] C. Kim, J. Y. Oh, and C. H. Choi, “Combined subspace method using global and local features for face recognition,” Proceedings of the International Joint Conference on Neural Networks , vol. 4, pp. 2030-2035, 2005.
    [55] Y. Fang, T. Tan, and Y. Wang, “Fusion of global and local features for face verification,” Proceeding of International Conference on Pattern Recognition, vol. 2, pp. 382-385 , 2002
    [56] Y. Lee, K. Lee, and S. Pan, “Local and global feature extraction for face recognition,” Proceeding of 5th International Conference on Audio- and Video-Based Biometric Person Authentication, pp. 21-40, 2005.
    [57] K. C. Lee, J. Ho, and D. J. Kriegman, “Acquiring linear subspaces for face recognition under variable lighting,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 684-698, 2005.

    無法下載圖示 校內:2024-12-31公開
    校外:不公開
    電子論文尚未授權公開,紙本請查館藏目錄
    QR CODE