簡易檢索 / 詳目顯示

回結果列表
研究生： 施凱翰
Shi, Kai-Han
論文名稱： 結合離散餘弦轉換與多尺度中心對稱局部二值模式特徵人臉辨識
Combining DCT and Multi-Scale CSLBP Feature Sets for Face Recognition
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： 2016
畢業學年度： 104
語文別： 英文
論文頁數： 49
中文關鍵詞： 人臉辨識 、離散餘弦轉換 、中心對稱局部二值模式 、值方圖
外文關鍵詞： face recognition, discrete cosine transform, CSLBP, histogram
相關次數： 點閱：91  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很重要的研究主題，可以廣泛的運用在各種領域當中，例如:資訊安全、警監系統、人機互動、出入境檢驗、上班打卡等等。
    在人臉辨識的方法中，使用局部二值模式或是中心對稱局部二值模式得到的人臉資訊是不夠全面且辨識的特徵不夠充足，要如何在少量的訓練樣本中得到更多的可辨識資訊，我們提出了結合離散餘弦轉換和多尺度中心對稱局部二值模式特徵混和的方式，藉由離散餘弦轉換來獲取人臉資訊的頻域資訊，藉由多尺度中心對稱局部二值模式獲取人臉空間資訊，對中心對稱局部二值模式，我們改變它的中心閥值獲取更多資訊。以ORL和AR的人臉資料庫，實驗我們的方法，驗結果顯示兩種特徵結合辨識效果優於原本單一的特徵，對雜訊的抵抗力也更強。

    Face recognition has been an active research area due to its wide range of application in information security, video surveillance systems, human-computer interaction, punching in, exit-entry inspection.
    In the face recognition method, using center-symmetry local binary patterns or local binary patterns obtains face information that is not comprehensive enough and sufficient enough to identify the characteristics. We try to get more face information in few training samples, proposing a combination of discrete cosine transform and multi-scale center- symmetry local binary patterns feature. The method use discrete cosine transform to obtain facial frequency-domain information and multi-scale center-symmetric local binary patterns to obtain facial space domain information. We use center-symmetric local binary patterns several times with changing its center of thresholds to obtain different scales face information. Experiments performed on ORL and AR facial database indicate that recognition rates of combination of two features are better than original single feature and our method is more robust to noise than others.

    Chapter 1 Introduction ..........1 1.1 Methodology ..........1 1.2 Review ..........2 1.3 Organization ..........6 Chapter 2 Face Recognition System ..........7 2.1 Normalization of a Face Image ..........7 2.1.1 Histogram Equalization ..........7 2.2 Feature Description ..........10 2.2.1 Local Binary Patterns ..........10 2.2.2 Center Symmetric Local Binary Patterns ..........12 2.2.3 Local Ternary Patterns ..........14 2.2.4 ɛ-LBP ..........16 2.3 Feature Extraction ..........17 2.3.1 Discrete Cosine Transform ..........17 2.3.2 Formal definition ..........18 2.3.3 2D Discrete Cosine Transform ..........19 2.4 Classifiers ..........21 2.4.1 Euclidean Distance ..........21 2.4.2 Chi-square Distance ..........21 2.4.3 K-Nearest Neighbors (KNN) ..........22 Chapter 3 Proposed Method ..........24 3.1 Proposed Method Framework ..........24 3.2 Feature Extraction and Selection Coefficients 26 3.3 Feature Description and Histogram ..........27 3.3.1 ɛ-CSLBP ..........27 3.3.2 Histogram ..........28 3.4 Classifiers ..........30 Chapter 4 Experimental Results and Discussions ..........32 4.1 Face Database ..........32 4.1.1 ORL face database ..........32 4.1.2 AR face database ..........33 4.2 Experimental results on the ORL database 33 4.2.1 Results by different sizes of cells ..........33 4.2.2 Results by different thresholds ..........35 4.2.3 Results of every threshold and size ..........36 4.2.4 Results of different weights and sizes by ɛ-CSLBP ..........37 4.2.5 Results of DCT ..........38 4.2.6 Results of our method ..........39 4.2.7 Face images with uniform noise ..........41 4.3 Experimental results on the AR database ..........43 4.3.1 Face images with uniform noise ..........43 References ..........46

    [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] Moonhwi Lee, Cheong Hee Park, “An Efficient Image Normalization Method for Face Recognition Under Varying Illuminations,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 4, pp. 711 - 720, 1997.
    [6] C.P. Chen and C.S. Chen, “Lighting Normalization with Generic Intrinsic Illumination Subspace for Face Recognition,” IEEE International Conference on Computer Vision, vol. 2, pp. 1089-1096, 2005.
    [7] Kelsey Ramírez-Gutiérrez, Daniel Cruz-Pérez, and Héctor Pérez-Meana, “Face Recognition and Verification Using Histogram Equalization,” ACS’ 10 Proceedings of the 10th WSEAS International Conference on Applied Computer Science, pp. 85-89, 2010.
    [8] T. Ojala, M. Pietikainen, T. Maeenpaa, “Multi-resolution 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.

    [9] T. Ahonen, A. Hadid, and M. Pietika¨inen, “Face Recognition with Local Binary Patterns” Proc. Eighth European Conf. Computer Vision, pp. 469-481, 2004.
    [10] M. Heikkila, M. Pietikainen, C. Schmid. “Description of interest regions with local binary patterns,” Pattern Recognition, vol. 42, pp. 425–436, 2009.
    [11]A. S. Samra, S. El Taweel Gad Allah and R. M. Ibrahim, “Face recognition using wavelet transform, fast Fourier transform and discrete cosine transform,” IEEE Midwest Symposium on Circuits and Systems,vol.1, pp. 272 - 275, 2003.
    [12] W. Hwang, H. Wang, H. Kim, S. C. Kee and J. Kim, “Face Recognition System Using Multiple Face Model of Hybrid Fourier Feature Under Uncontrolled Illumination Variation,” IEEE Transactions on Image Processing, vol.20, pp. 1152 – 1165, 2010.
    [13] 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.
    [14] 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, vol. 36, pp. 458-466, 2006.
    [15] Z. Yankun and L. Chongqing “Efficient face recognition method based on DCT and LDA,” Journal of Systems Engineering and Electronics, vol. 15, pp. 211-216, 2004.
    [16] 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.
    [17] J. Ravi and S. S. Tevaramani, “Face Recognition using DT-CWT and LBP Features,” 2012 International Conference on Computing, Communication and Applications, pp.1- 6, 2012.
    [18] 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,” Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on, vol. 1, pp.786 – 791, 2005.
    [19] S. Liao, X. Zhu, Z. Lei, L. Zhang, and S. Li, “Learning multi-scale block local binary patterns for face recognition,” ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics, pp. 828-837, 2007.
    [20] 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.
    [21] M. Heikkilä, M. Pietikäinen, and C. Schmid, “Description of interest regions with local binary patterns,” Pattern Recognition, vol. 42, pp. 425-436, 2009.
    [22] A. Hafiane, G. Seetharaman, and B. Zavidovique, “Median binary pattern for textures classification,” Image Analysis and Recognition, pp. 387-398, 2007.
    [23] F. Ahmed, E. Hossain, A. Bari, and A. Shihavuddin, “Compound local binary pattern (CLBP) for robust facial expression recognition,” Computational Intelligence and Informatics (CINTI), 2011 IEEE 12th International Symposium on, pp. 391-395, 2011.
    [24] 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.
    [25] W. Yang and C. Sun, “Face recognition using improved local texture patterns,” Intelligent Control and Automation (WCICA), 2011 9th World Congress on, pp. 48-51, 2011.
    [26] 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.
    [27] Zilong Cheng, Jinguang Sun, and Xiuyu Lei, “U-face of Applied Research in The Face Recognition,” IEEE 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), vol. 5, pp. V5-15 - V5-18, 2010.
    [28]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.
    [29] Z. Wang and B. R. Hunt, “The discrete cosine transform-A new version,” Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '83, vol. 8, pp. 1256 – 1259, 1983.
    [30] Z. Wang, “Fast algorithms for the discrete W transform and for the discrete Fourier transform,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 32, pp. 803-816, 1984.
    [31] N.J. August and Dong Sam Ha, “Low power design of DCT and IDCT for low bit rate video codecs,” IEEE Transactions on Multimedia, vol. 6, pp. 414 – 422, 2004.
    [32] N. Ahmed, T. Natarajan, and K. R. Rao, “Discrete cosine transform,” IEEE Transactions on Computers, vol. 23, pp. 90-93, 1974.
    [33] D. Kunz, “An orientation-selective orthogonal lapped transform,” IEEE Transactions on Image Processing, vol. 17, pp.1313-1322, 2008.

    無法下載圖示 校內：2019-07-01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