臉部辨識和臉部表情辨識中實域和複域的約束矩陣分解

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：37

、訪客IP：52.15.56.12

姓名

普蒂雅(Diyah Utami Kusumaning Putri) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

臉部辨識和臉部表情辨識中實域和複域的約束矩陣分解
(Constrained Matrix Factorization in Real and Complex Domain for Face and Facial Expression Recognition)

相關論文

★ Single and Multi-Label Environmental Sound Recognition with Gaussian Process	★ 波束形成與音訊前處理之嵌入式系統實現
★ 語音合成及語者轉換之應用與設計	★ 基於語意之輿情分析系統
★ 高品質口述系統之設計與應用	★ 深度學習及加速強健特徵之CT影像跟骨骨折辨識及偵測
★ 基於風格向量空間之個性化協同過濾服裝推薦系統	★ RetinaNet應用於人臉偵測
★ 金融商品走勢預測	★ 整合深度學習方法預測年齡以及衰老基因之研究
★ 漢語之端到端語音合成研究	★ 基於 ARM 架構上的 ORB-SLAM2 的應用與改進
★ 基於深度學習之指數股票型基金趨勢預測	★ 探討財經新聞與金融趨勢的相關性
★ 基於卷積神經網路的情緒語音分析	★ 運用深度學習方法預測阿茲海默症惡化與腦中風手術存活

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 ( 永不開放)

摘要(中)

本研究提出了複雜域上的矩陣分解的新方法，以獲得提取的特徵和係數矩陣，在臉部辨識和臉部表情辨識問題中具有高識別結果。基於複數的歐拉表示將實數據矩陣變換為複數。

基礎複雜矩陣分解（CMF）使用幾個約束進行修改並在本研究中進行了研究。使用嶺項（SCMF-L2）將基本 CMF修改為稀疏複矩陣分解，其在係數中添加稀疏 L2 範數約束。本研究也開發了新穎的約束，它在稱為空間約束複雜矩陣分解（SpatialCMF）的基礎矩陣上實施像素色散懲罰。本研究構建了新的約束，它使用像素圖像表示和類註釋約束的組合來訓練稱為耦合複矩陣分解（CoupledCMF）的數據。本研究所提出的方
法與普遍大家所使用的 NMF方法和 CMF方法的擴張相比較，包括稀疏複矩陣分解（SCMF）和分別添加稀疏 L1 範數和圖形約束的圖複雜矩陣分解（GCMF）。梯度下降法則是用於解決優化問題。

臉部表情辨識場景的實驗包含整個臉部和遮蔽臉部的識別，本研究提出的方法比較普遍大家使用的 NMF和 CMF方法提升了更好的識別結果。此方法也達到停止條件，並且比 NMF和 CMF方法的擴張快得許多。

摘要(英)

This work proposes novel methods of matrix factorization on the complex domain to obtain both extracted features and coefficient matrix with high recognition results in a face recognition and facial expression recognition problems. The real data matrix is transformed into a complex number based on the Euler representation of complex numbers.
The basic complex matrix factorization (CMF) is modified using several constraints and is investigated in this study. The basic CMF is modified into Sparse Complex Matrix Factorization using Ridge Term (SCMF-L2) which adds sparse L2-norm constraint in the coefficient. This study also develops novel constraint which enforces pixel dispersion penalty on the basis matrix called Spatial Constrained Complex Matrix Factorization (SpatialCMF). This study also builds novel constraint which uses the combination of pixel images representation and class annotation constraints for training data named as Coupled Complex Matrix Factorization (CoupledCMF). The proposed methods compare with prevalent NMF methods and extensions of CMF methods, including sparse complex matrix factorization (SCMF) and graph complex matrix factorization (GCMF) which adds sparse L1-norm and graph constraints, respectively. The gradient descent method is used to solve optimization problems.
Experiments on face recognition and facial expression recognition scenarios that involve a whole face and an occluded face reveal that the proposed methods provide better recognition results that common NMF and CMF methods. The proposed methods also reach the stopping condition and converge much faster than the extensions of NMF and CMF methods.

關鍵字(中)

★ 特徵提取
★ 非負矩陣分解
★ 複矩陣分解
★ 投影梯度下降
★ 臉部辨識
★ 臉部表情辨識

關鍵字(英)

★ feature extraction
★ non-negative matrix factorization
★ complex matrix factorization
★ projected gradient descent
★ face recognition
★ facial expression recognition

論文目次

摘要 i
Abstract ii
Acknowledgements iii
Contents iv
List of Symbols and Abbreviations vii
List of Figures ix
List of Tables x
CHAPTER 1 1
1.1 Motivation 1
1.2 Research Problem 3
1.3 Research Objective 4
1.3.1 General Objective 4
1.3.2 Specific Objectives 4
1.4 Thesis Overview 5
1.4.1 Chapter 2 Related Work 5
1.4.2 Chapter 3 Theoretical Basis 5
1.4.3 Chapter 4 Experiments 5
1.4.4 Chapter 5 Results and Analysis 5
1.4.5 Chapter 6 Conclusion and Future Work 5
CHAPTER 2 6
CHAPTER 3 20
3.1 Matrix Theory 20
3.1.1 Matrix Manipulation 20
3.1.2 Vector Spaces 23
3.1.3 Norm 24
3.2 Lagrangian Duality and the Karush-Kuhn-Tucker (KKT Conditions) 24
3.3 Complex Analysis and Optimization in the Complex Domain 26
3.3.1 Wirtinger Calculus and Differentials of Real-Valued Functions 26
3.3.2 Optimization in Complex Domain 27
3.4 Nonnegative Matrix Factorization 30
3.4.1 Introductions of Nonnegative Matrix Factorization 30
3.4.2 Nonnegative Matrix Factorization 31
3.4.3 Selected Applications of NMF 35
3.4.4 Extensions of Nonnegative Matrix Factorization Methods 35
3.5 Complex Matrix Factorization 42
3.5.1 Extensions of Complex Matrix Factorization Methods 45
3.6 Recognition Workflow 47
3.6 Performance Measure 49
CHAPTER 4 51
4.1 Datasets 51
4.2 Proposed Methods 52
A. Sparse Complex Matrix Factorization using Ridge Term (SCMF-L2) 52
B. Spatial Complex Matrix Factorization (SpatialCMF) 53
C. Coupled Complex Matrix Factorization (CoupledCMF) 54
4.3 Comparison of Methods and Experimental Settings 56
4.4 Experimental Methods 59
A. Different Subspace Dimensions 59
B. Different K-Fold Cross-Validation 60
C. Recognition of Occluded Faces 60
D. Reconstructed Images 60
E. Convergence Time 61
CHAPTER 5 62
5.1 Face Recognition on Un-occluded ORL Dataset with Different Subspace Dimensions 62
5.2 Facial Expression Recognition on Un-Occluded JAFFE Dataset with Different Subspace Dimensions 65
5.3 Face Recognition on Un-occluded ORL Dataset with Different K-Fold Cross-Validation 68
5.4 Facial Expression Recognition on Un-occluded JAFFE dataset with Different K-Fold Cross-Validation 69
5.5 Learned Basis Images on ORL dataset 70
5.6 Learned Basis Images on JAFFE dataset 71
5.7 Face Recognition on Occluded ORL Dataset 72
5.8 Facial Expression Recognition on Occluded JAFFE dataset 73
5.9 Reconstructed Images on ORL dataset 75
5.10 Reconstructed Images on JAFFE dataset 76
5.11 Convergence Time on ORL Dataset 77
5.12 Convergence Time on JAFFE Dataset 80
CHAPTER 6 84
6.1 Result Summary 84
6.2 Limitation 85
6.3 Future Research 85
Bibliographies 86

參考文獻

[1] L. Eldén, Matrix Methods in Data Mining and Pattern Recognition (Fundamentals of Algorithms). Philadelphia, USA: Society for Industrial and Applied Mathematics, 2007.
[2] D. D. Lee and H. S. Seung, “Learning the Parts of Objects by Non-negative Matrix Factorization,” Nature, vol. 401, no. 6775, pp. 788–791, 1999.
[3] D. Lee and S. Seung, “Algorithms for Non-negative Matrix Factorization,” Adv. Neural Inf. Process. Syst. 13, no. 1, pp. 556–562, 2001.
[4] W. Xu, X. Liu, and Y. Gong, “Document Clustering Based on Non- Negative Matrix Factorization,” in Proc. ACM Conf. Research and Development in Information Retrieval (SIGIR), 2003, pp. 267–273.
[5] V. P. Pauca, F. Shahnaz, M. W. Berry, and R. J. Plemmons, “Text Mining using Non-Negative Matrix Factorizations,” in Proc. SIAM Int’l Conf. Data Mining, 2004, pp. 452–456.
[6] J.-P. Brunet, P. Tamayo, T. R. Golub, and J. P. Mesirov, “Metagenes and molecular pattern discovery using matrix factorization.,” in Proceedings of the National Academy of Sciences of the United States of America, 2004, vol. 101, no. 12, pp. 4164–4169.
[7] H. Kim and H. Park, “Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis,” Bioinformatics, vol. 23, no. 12, pp. 1495–1502, 2007.
[8] J.-X. Liu, Y.-L. Gao, Y. Xu, D. Wang, J. Yu, and C.-H. Zheng, “Regularized Non-Negative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Samples: A Survey,” IEEE/ACM Trans. Comput. Biol. Bioinforma., vol. 15, no. 3, pp. 974–987, 2018.
[9] D. Guillamet, B. Schiele, and J. Vitria, “Analyzing non-negative matrix factorization for image classification,” IEEE, pp. 116–119, 2003.
[10] D. Guillamet and J. Vitrià, “Non-negative Matrix Factorization for Face Recognition,” in Escrig M.T., Toledo F., Golobardes E. (Eds) Topics in Artificial Intelligence. CCIA 2002. Lecture Notes in Computer Science, vol 2504. Springer, Berlin, Heidelberg, 2002, pp. 336–344.
[11] Z. Ying and G. Zhang, “Facial expression recognition based on NMF and SVM,” 2009 Int. Forum Inf. Technol. Appl., pp. 612–615, 2009.
[12] H. Kameoka, N. Ono, K. Kashino, and S. Sagayama, “Complex NMF: A new sparse representation for acoustic signals,” 2009 IEEE Int. Conf. Acoust. Speech Signal Process., pp. 3437–3440, 2009.
[13] B. J. King and L. Atlas, “Single-channel source separation using complex matrix factorization,” IEEE Trans. Audio, Speech Lang. Process., vol. 19, no. 8, pp. 2591–2597, 2011.
[14] A. Mesaros, T. Heittola, O. Dikmen, and T. Virtanen, “Sound Event Detection in Real Life Recordings using Coupled Matrix Factorization of Spectral Representations and Class Activity Annotations,” 2015 IEEE Int. Conf. Acoust. Speech Signal Process., pp. 151–155, 2015.
[15] Y. X. Wang and Y. J. Zhang, “Nonnegative matrix factorization: A comprehensive review,” IEEE Trans. Knowl. Data Eng., vol. 25, no. 6, pp. 1336–1353, 2013.
[16] P. O. Hoyer, “Non-Negative Matrix Factorization with Sparseness Constraints,” J. Mach. Learn. Res. 5, vol. 5, pp. 1457–1469, 2004.
[17] J. Eggert and E. Korner, “Spare Coding and NMF,” 2004 IEEE Int. Jt. Conf. Neural Networks, pp. 2529–2533, 2004.
[18] C. Ding, T. Li, W. Peng, and H. Park, “Orthogonal nonnegative matrix t-factorizations for clustering,” in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, 2006, pp. 126–135.
[19] S. Choi, “Algorithms for orthogonal nonnegative matrix factorization,” 2008 IEEE Int. Jt. Conf. Neural Networks, pp. 1828–1832, 2008.
[20] N. Guan, D. Tao, Z. Luo, and B. Yuan, “Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent,” IEEE Trans. Image Process., vol. 20, no. 7, pp. 2030–2048, 2011.
[21] D. Cai, X. He, X. Wu, and J. Han, “Non-negative matrix factorization on manifold,” in Proceedings - IEEE International Conference on Data Mining (ICDM ’08), 2008, pp. 63–72.
[22] D. Cai, X. He, J. Han, and T. S. Huang, “Graph regularized nonnegative matrix factorization for data representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 33, no. 8, pp. 1548–1560, 2011.
[23] V.-H. Duong, Y.-S. Lee, B.-T. Pham, S. Mathulaprangsan, P. T. Bao, and J.-C. Wang, “Complex Matrix Factorization for Face Recognition,” 2016. [Online]. Available: http://arxiv.org/abs/1612.02513.
[24] S. Liwicki, G. Tzimiropoulos, S. Zafeiriou, and M. Pantic, “Euler principal component analysis,” Int. J. Comput. Vis., vol. 101, no. 3, pp. 498–518, 2013.
[25] R. Peharz and F. Pernkopf, “Sparse nonnegative matrix factorization with L0-constraints,” Neurocomputing, vol. 80, pp. 38–46, 2012.
[26] B. Shen, B. Di Liu, Q. Wang, and R. Ji, “Robust nonnegative matrix factorization via L1 norm regularization by multiplicative updating rules,” IEEE Int. Conf. Image Process., vol. 2, no. 2, pp. 5282–5286, 2014.
[27] F. Nie, H. Huang, X. Cai, and C. Ding, “Efficient and Robust Feature Selection via Joint L21-Norms Minimization,” Adv. Neural Inf. Process. Syst., vol. 23, pp. 1813–1821, 2010.
[28] N. B. Erichson, A. Mendible, S. Wihlborn, and J. N. Kutz, “Randomized nonnegative matrix factorization,” Pattern Recognit. Lett., vol. 104, pp. 1–7, 2018.
[29] Y. Li and A. Ngom, “Versatile sparse matrix factorization and its applications in high-dimensional biological data analysis,” in PRIB’13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics, 2013, pp. 91–101.
[30] Z. Lu, Y. Sun, Y. Hu, and B. Yin, “Robust face recognition based L21-norm sparse representation,” 2014 5th Int. Conf. Digit. Home, pp. 25–29, 2014.
[31] W. Liu, N. Zheng, and X. Lu, “Non-negative matrix factorization for visual coding,” in Proceedings IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2003), 2003, pp. 293–296.
[32] S. Z. Li, Xin Wen Hou, Hong Jiang Zhang, and Qian Sheng Cheng, “Learning spatially localized, parts-based representation,” in Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), 2001, pp. 207–212.
[33] W. S. Zheng, J. Lai, S. Liao, and R. He, “Extracting non-negative basis images using pixel dispersion penalty,” Pattern Recognit., vol. 45, no. 8, pp. 2912–2926, 2012.
[34] V.-H. Duong, M.-Q. Bui, P. T. Bao, and J.-C. Wang, “A New Constrained Nonnegative Matrix Factorization for Facial Expression Recognition,” Int. Conf. Orange Technol., pp. 79–82, 2017.
[35] Y. K. Yılmaz, A. T. Cemgil, and U. Simsekli, “Generalised Coupled Tensor Factorisation,” NIPS, 2011.
[36] I. Sobieraj and M. D. Plumbley, “Coupled Sparse NMF vs. Random Forest Classification for Real Life Acoustic Event Detection,” in Detection and Classification of Accoustic Scenes and Events, 2016.
[37] M. Rajapakse and L. Wyse, “Face Recognition with Non-negative Matrix Factorization,” in Proceedings of SPIE - The International Society for Optical Engineering, 2003, vol. 5150, pp. 1838–1847.
[38] G. D. Allen, “Linear Algebra for Applications,” 2003. [Online]. Available: https://www.math.tamu.edu/~dallen/m640_03c/readings.htm.
[39] D. Cherney, T. Denton, R. Thomas, and A. Waldron, Linear Algebra, 1st ed. California, USA: CreateSpace Independent Publishing Platform, 2013.
[40] S. Boyd and L. Vandenberghe, Convex optimization theory. New York: Cambridge University Press, 2004.
[41] J. M. Borwein and A. S. Lewis, Convex Analysis and Nonlinear Optimization, 2nd ed. Springer-Verlag New York, 2006.
[42] E. Kreyszig, Advanced Engineering Mathemathics, 10th ed. United States, America: John Willey and Sons, Inc., 2011.
[43] B. P. Palka, An Introduction to Complex Function Theory. Springer, 1991.
[44] M. J. Ablowitz and A. S. Fokas, Complex Variables: Introduction and Applications, 2nd ed. New York: Cambridge University Press, 2003.
[45] M. F. Amin, M. I. Amin, A. Y. H. Al-Nuaimi, and K. Murase, “Wirtinger Calculus Based Gradient Descent and Levenberg-Marquardt Learning Algorithms in Complex-Valued Neural Networks,” in Neural Information Processing. 18th ICONIP 2011. Lecture Notes in Computer Science, vol 7062., 2011, pp. 550–559.
[46] A. Hjørungnes and D. Gesbert, “Complex-Valued Matrix Differentiation: Techniques and Key Results,” IEEE Trans. Signal Process., vol. 55, no. 6, pp. 2740–2746, 2007.
[47] J. R. Magnus and H. Neudecker, Matrix Differential Calculus with Applications in Statistics and Econometrics., 3rd ed. Essex, England: John Wiley & Sons, 2007.
[48] G. Strang, Linear Algebra and Its Applications, 4th ed. Thomson, Brooks/Cole, 2006.
[49] C.-J. Lin, “Projected Gradient Methods for Non-negative Matrix Factorization,” Neural Comput., vol. 19, no. 10, pp. 2756–2779, 2007.
[50] P. Paatero and U. Tapper, “Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values,” Environmetrics, vol. 5, pp. 111–126, 1994.
[51] K. T. Islam, R. G. Raj, and A. Al-Murad, “Performance of SVM, CNN, and ANN with BoW, HOG, and Image Pixels in Face Recognition,” 2nd Int. Conf. Electr. Electron. Eng., pp. 1–4, 2017.
[52] N. Alam, M. Sarim, A. B. Shaikh, S. K. Raffat, A. Nadeem, and K. Ahsan, “Face Recognition Using Non-negative Matrix Factorization ( NMF ) An Analysis of Order of Decomposition on Recognition Rate,” Res. J. Recent Sci., vol. 4, no. 4, pp. 77–82, 2015.
[53] M. J. Zaki and W. M. Jr., Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, 2014.
[54] D. Cielen, A. D. B. Meysman, and M. Ali, Introduction Data Science: Big Data, Machine Learning, and More, using Python Tools. Shelter Island, New York: Manning Publications Co., 2016.
[55] “The ORL database of Face.” [Online]. Available: https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html.
[56] “The Japanese Female Facial Expression (JAFFE) database.” [Online]. Available: http://www.kasrl.org/jaffe.html.

指導教授

王家慶

審核日期

2019-7-31

推文