應用特徵線為基礎之度量學習框架於身份識別

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姓名

謝正達(Cheng-Ta Hsieh) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

應用特徵線為基礎之度量學習框架於身份識別
(A Metric Learning Framework based on Feature Line Embedded for Person Identification)

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摘要(中)

本論文提出特徵線度量學習框架，應用於身份辨識，包含有人臉驗證(face verification)、人臉辨識(face recognition)與行人識別(person re-identification)。傳統進行圖形識別會使用度量學習進行樣本之間的相似度的計算。進行影像識別時，會使用高維度特徵向量表示各影像的特性，也相對需要大量運算資源。為了解決高維度產生的高運算量問題，鑑別分析方法(discriminant analysis)被應用於降低特徵維度並提高資料的鑑別度。常見的鑑別學習方法有線性鑑別分析及區域保留投影等方式。有別於傳統使用樣本點到樣本族群中心或樣本點到樣本點計算特徵向量各個維度的權重值，本論文提出以樣本點到樣本特徵線(feature line embedded)取代點到中心或點到點的計算方式。本論文第一個主題為人臉驗證，提出特徵線嵌入偏頗鑑別方法(FLE-BDA)，計算測試影像與指定影像的相似度，驗證是否為相同身份。第二個主題為人臉辨識，本論文結合核空間(kernelization)與最近特徵線提出核空間最近特徵線嵌入分析(KNFLE)，測試影像與資料庫影像經過投影降維後，以歐氏距離比較兩者的相似度，藉以辨識測試影像的身份。第三個主題為行人再識別，則使用特徵線方式計算樣本之類的權重關係，同時計算樣本的機率分布，嵌入馬氏距離。測試樣本與資料庫樣本相似度，用來計算不同攝影機之間的行人影像相似度。上述問題人臉驗證及辨識使用Yale B、ORL及CMU PIE人臉資料庫進行效果驗證。行人再識別問題我們使用VIPeR與QMUL GRID行人影像資料庫與相關研究進行比較。

摘要(英)

This dissertation proposed a metric learning framework based on feature line embedded for person identification. In order to avoid the high computational complexity required for high dimensional features, the discriminant analysis methods was used to transform data from high dimension space to low dimension space. The discriminant analysis can reduce the feature dimension while increasing the class separation ability.
The point to line (P2L) strategy can be used to find the effective and discriminant transformation in eigenspace. So that, P2L was integrated into biased discriminant analysis algorithm to solve face verification firstly. Secondly, we used P2L in kernel space to deal the face recognition problem. Thirdly, the P2L based quadratic discriminant analysis was used to solve the person re-identification issue.
In face verification, the biased discriminant analysis focus on positive samples. The projection matrix decrease the distance between positive samples and positive center. Meanwhile, the distance between negative samples and positive center were increased. We used the concept of biased discriminant analysis to propose the P2L based BDA to solve the face verification issue.
In face recognition, since face recognition is a multi-class problem instead of two-class in face verification. Therefore, we apply the feature line embedded method preserving the local structure information in kernel space for increasing the recognition rate.
In person re-identification, the quadratic discriminant analysis based on P2L consider the data distribution and the local structure information at the same time, the performance of person re-identification could be raised.
In the experiments, we discuss the effects of various discriminant learning method on face verification, face recognition, and person re-identification. In face verification and recognition issue, the toy samples experiments showed the data distribution of discriminant analysis method. The real facial image database: Yale B, ORL, and CMU PIE face database used to evaluate the performance of face verification and recognition. The equal error rate of face verification was used to evaluate dimensionality reduction method. The recognition rate showed the effect of face recognition.
The VIPeR and QMUL GRID pedestrian dataset were used to evaluate the efficiency of person re-identification algorithm. The experimental results showed our proposed P2L strategy based quadratic discriminant analysis was the best.

關鍵字(中)

★ 身份識別
★ 度量學習
★ 特徵線
★ 人臉驗證
★ 人臉辨識
★ 行人識別

關鍵字(英)

★ Person Identification
★ Metric Learning
★ Feature Line
★ Face Verification
★ Face Recognition
★ Person Re-Identification

論文目次

Abstract i
Chapter 1 Introduction 1
Chapter 2 Related Works 9
2.1 Discriminant Learning Approaches 9
2.1.1 Linear discriminant analysis (LDA) 9
2.1.2 Biased discriminant analysis (BDA) 10
2.1.3 Local preserve projection (LPP) 10
2.1.4 Nearest feature line embedded 12
2.2 Kernel Space 13
2.3 Distance Measurement Methods 14
2.3.1 Mahalanobis distance 14
2.3.2 Keep It Simple and Straightforward MEtric (KISSME) 15
Chapter 3 Face Verification Using Feature Line Based Biased Discriminant Analysis 16
3.1 Nearest Feature Embedded Biased Discriminant Analysis 17
3.2 Optimal Linear Analysis 18
3.3 Comparison of FLE-BDA and Two-class NFL Classifier 21
Chapter 4 Face Recognition Using Kernel-based Nearest Feature Line Embedded 22
Chapter 5 Person Re-Identification Using Feature Line Embedded Quadratic Discriminant Analysis 24
Chapter 6 Experimental Results 26
6.1 The Experimental Results of Face Verification 26
6.1.1 Toy samples 26
6.1.2 Face Verification Evaluation 30
6.2 The Experimental Results of Face Recognition 48
6.2.1 Toy samples 48
6.2.2 Face recognition evaluation 49
6.3 Person Re-Identification 52
Chapter 7 Conclusions 57
References 58

參考文獻

[1] M. Dikmen, E. Akbas, T. S. Huang, and N. Ahuja, “Pedestrian recognition with a learned metric,” in Proceedings of Asian Conference on Computer Vision 2010, pp. 501–512, 2010.
[2] W. S. Zheng, S. G. Gong, and T. Xiang, “ Reidentification by relative distance comparison,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 3, pp. 653-668, 2013.
[3] M. Kostinger, M. Hirzer, P. Wohlhart, P. M. Roth, and H. Bischof, “Large scale metric learning from equivalence constraints,” in Proceedings. of IEEE Conference on Computer Vision and Pattern Recognition, 2012.
[4] M. Hirzer, P. M. Roth, M. Kostinger, and H. Bischof, “Relaxed pairwise learned metric for person re-identification” In European Conference on Computer Vision, 2012.
[5] Z. Li, S. Chang, F. Liang, T. S. Huang, L. Cao, and J. R. Smith, “Learning locally-adaptive decision functions for person verification,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2013.
[6] S. Liao, Y. Hu, X. Zhu, and S. Z. Li, “Person re-identification by local maximal occurrence representation and metric learning,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2015.
[7] M. Turk and A.P. Pentland, “Face Recognition Using Eigenfaces,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 586-591, 1991.
[8] 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, no. 7, pp. 711-720, July 1997.
[9] H. Cevikalp, M. Neamtu, M. Wikes, and A. Barkana, “Discriminative Common Vectors for Face Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 1, pp. 4-13, Jan. 2005.
[10] S. Prasad and L. Mann Bruce, “Information Fusion in Kernel-Induced Spaces for Robust Subpixel Hyperspectral ATR,” IEEE Transactions on Geoscience Remote Sensing Letters, vol. 6, no. 3, pp. 572-576, Jul. 2009.
[11] X. He, S. Yan, Y. Ho, P. Niyogi, and H.J. Zhang, “Face Recognition Using Laplacianfaces,” IEEE Transactions on Pattern Analysis Machince and Intelligence., vol. 27, no. 3, pp. 328-340, Mar. 2005.
[12] S.Z. Li, “Face Recognition Based on Nearest Linear Combinations,” in Proceedings of Computer Vision and Pattern Recognition, pp. 839-844, 1998.
[13] S.Z. Li and J. Lu, “Face Recognition Using the Nearest Feature Line Method,” IEEE Transactions on Neural Networks, vol. 10, no. 2, pp. 439-433, Mar. 1999.
[14] S.Z. Li, K.L. Chan, and C.L. Wang, “Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval,” IEEE Transactions on . Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1335-1339, Nov. 2000.
[15] Y. N. Chen, C. C. Han, C. T. Wang, and K. C. Fan, “Face Recognition Using Nearest Feature Space Embedding,” IEEE Transactions on . Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp.1073 - 1086, Jun. 2011.
[16] B. Boots, and G. J. Gordon, “Two-manifold Problems with Applications to Nonlinear System Identification,” in Proceedings of 29th International Conference on Machine Learning, United Kingdom, Jun. 26 - Jul. 1, 2012.
[17] Y. Y. Lin, T. L. Liu, and C. S. Fuh, “Multiple Kernel Learning for Dimensionality Reduction,” IEEE Transactions on . Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp.1147 - 1160, Jun. 2011.
[18] J. Li, P. R. Marpu, A. Plaza, J. M. Bioucas-Dias, and J. A. Benediktsson, “Generalized Composite Kernel Framework for Hyperspectral Image Classification,” IEEE Transactions on Geoscience. Remote Sensing, vol. 51, no. 9, pp.4816 - 4829, Sep. 2013.
[19] Y. Chen, N. M. Nasrabadi, and T. D. Tran, “Hyperspectral Image Classification via Kernel Sparse Representation,” IEEE Transactions on Geoscience. Remote Sensing, vol. 51, no. 1, pp.217 - 231, Jan. 2013.
[20] L. Zhang, L. Zhang, D. Tao, and X. Huang, “On Combining Multiple Features for Hyperspectral Remote Sensing Image Classification,” IEEE Transactions on Geoscience Remote Sensing, vol. 50, no. 3, pp.879-893, Jan. 2012.
[21] Y. L. Chang, J. N. Liu, C. C. Han, and Y. N. Chen, “Hyperspectral Image Classification Using Nearest Feature Line Embedding Approach,” IEEE Transactions on Geoscience and Remote Sensing, vol. 52, no. 1, pp. 278-287, 2014.
[22] X. Zhou and T. Huang, “Small Sample Learning During Multimedia Retrieval Using BiasMap,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Dec. 2001, vol. 1, pp. I-11–I-17.
[23] D. Tao, X. Tang, X. Li, and Y. Rui, “Kernel Direct Biased Discriminant Analysis: A New Content-based Image Retrieval Relevance Feedback Algorithm,” IEEE Transactions on Multimedia, vol. 8, no. 4, pp. 716–724, Aug. 2006.
[24] D. Xu, S. Yan, D. Tao, S. Lin, and H. Zhang, “Marginal Fisher Analysis and Its Variants for Human Gait Recognition and Content-based Image Retrieval,” IEEE Transactions on Image Processing, vol. 16, no. 11, pp. 2811–2821, Nov. 2007.
[25] X. F. He, D. Cai, and J. W. Han, “Learning a Maximum Margin Subspace for Image Retrieval,” IEEE Transactions on Knowledge and Data Engineering, vol. 20, no. 2, pp. 189–201, Feb. 2008.
[26] L. Zhang, L. Wang, and W. Lin, “Semisupervised Biased Maximum Margin Analysis for Interactive Image Retrieval,” IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 2294–2308, Apr. 2012.
[27] Y. Y. Lin, T. L. Liu, and H. T. Chen, “Semantic Manifold Learning for Image Retrieval,” in Proceedings of ACM International Conference on Multimedia, 2005, pp. 249–258.
[28] S. Yan et al., “Graph Embedding and Extensions: General Framework for Ddimensionality Reduction,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 40–51, Jan. 2007.
[29] Bedagkar-Gala and Shishir K. Shah, “A survey of approaches and trends in person re-identification,” Journal of Image and Vision Computing, vol. 32, pp. 270–286, 2014.
[30] T. F. Zhou, M. B. Qi, J. G. Jiang, X. Wang, S. Hao, and Y. Jin, “Person re-identification based on nonlinear ranking with difference vectors,” Information Sciences, vol. 279, pp. 604-614, 2014.
[31] M. Hirzer, C. Beleznai, P. Roth, and H. Bischof, “Person re-identification by discriptive and discriminative classification,” in Proceedings of Scandinavian Conference on Image Analysis (SCIA), 2011.
[32] P. M. Roth, M. Hirzer, M. Koestinger, C. Beleznai, and H. Bischof, “Mahalanobis distance learning for person re-Identification,” in Person Re-Identification, pages 247-267, Springer, 2014.
[33] S. Pedagadi, J. Orwell, S. Velastin, and B. Boghossian, “Local fisher discriminant analysis for pedestrian re-identification,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2013.
[34] M. Köstinger, M. Hirzer, P. Wohlhart, P. M. Roth, H. Bischof, “Large scale metric learning from equivalence constraints,” in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, (CVPR) 2012.
[35] K. Q. Weinberger, J. Blitzer, and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification,” in Advances in Neural Information Processing System, 2006.
[36] K. Q. Weinberger and L. K. Saul, “Fast solvers and efficient implementations for distance metric learning,” in Proceedings of the 25th International Conference on Machine Learning, pp. 1160-1167, 2008.
[37] J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, “Information theoretic metric learning,” in Proceedings of IEEE International Conference on Machine Learning, 2007.
[38] M. Guillaumin, J. Verbeek, and C. Schmid, “Is that you? Metric learning approaches for face identification,” in Proceedings of IEEE International Conference on Computer Vision, 2009.
[39] S. Liao, G. Zhao, V. Kellokumpu, M. Pietik¨ainen, and S. Z. Li, “Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, June 2010.
[40] P. C. Mahalanobis, “On The Generalised Distance in Statistics,” in Proceedings of the National Institute of Sciences of India, vol. 2, no. 1, pp. 49-55, 1936.
[41] A. S. Georghiades and P. N. Belhumeur, “From Few to Many: Illumination Cones Models for Face Recognition Under Variable Lighting and Pose,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 643-660, 2001.
[42] K. C. Lee, J. Ho, and D. J. Kriegman, “Acquiring Linear Subspace for Face Recognition Under Variable Lighting,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 684-698, 2005.
[43] The Olivetti & Oracle Research Laboratory Face Database of Faces, http://www.cam-orl.co.uk/facedatabase.html, 1994.
[44] T. Sim, S. Baker, and M. Bsat, “The CMU Pose, Illumination, and Expression Database,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1615-1618, Dec. 2003.
[45] D. Gray, S. Brennan, and H. Tao, “Evaluating appearance models for recognition, reacquisition, and tracking,” in Proceedings of IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, 2007.
[46] C. C. Loy, T. Xiang, and S. Gong, “Multi-camera activity correlation analysis,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1988-1995, 2009.

指導教授

范國清、韓欽銓(Kuo-Chin Fan Chin-Chuan Han)

審核日期

2017-7-24

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