博碩士論文 93542003 詳細資訊



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：78 、訪客IP：18.191.189.85
姓名 王彥棋(Yen-chi Wang)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 局部區塊相減樣式使用於性別辨識
(Local Block Difference Pattern for Gender Classification)
相關論文
★ 使用視位與語音生物特徵作即時線上身分辨識★ 以影像為基礎之SMD包裝料帶對位系統
★ 手持式行動裝置內容偽變造偵測暨刪除內容資料復原的研究★ 基於SIFT演算法進行車牌認證
★ 基於動態線性決策函數之區域圖樣特徵於人臉辨識應用★ 基於GPU的SAR資料庫模擬器：SAR回波訊號與影像資料庫平行化架構 (PASSED)
★ 利用掌紋作個人身份之確認★ 利用色彩統計與鏡頭運鏡方式作視訊索引
★ 利用欄位群聚特徵和四個方向相鄰樹作表格文件分類★ 筆劃特徵用於離線中文字的辨認
★ 利用可調式區塊比對並結合多圖像資訊之影像運動向量估測★ 彩色影像分析及其應用於色彩量化影像搜尋及人臉偵測
★ 中英文名片商標的擷取及辨識★ 利用虛筆資訊特徵作中文簽名確認
★ 基於三角幾何學及顏色特徵作人臉偵測、人臉角度分類與人臉辨識★ 一個以膚色為基礎之互補人臉偵測策略
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 (全文檔遺失)
請聯絡國立中央大學圖書館資訊系統組 TEL:(03)422-7151轉57422，或E-mail聯絡
摘要(中) 近年來，圖形識別已經廣泛的應用在日常生活上。像是國道上的自動收費系統，除了利用RFID感應之外，還必須結合車牌的偵測和辨識，才能確保系統的穩定度。或是以掌紋，虹膜和人臉這些生物特徵為基礎的門禁系統，透過這些生物特徵來做認證進而達到門禁控管的目的。這些應用中最關鍵的技術為如何擷取出具有辨識能力的特徵，在這些特徵中局部紋理特徵的方式，更是廣泛且頻繁的應用在各種研究中，其中最為著名的是LBP(local Binary Pattern)，許多的研究學者也發展出基於LBP特點的各種局部紋理特徵，在本研究中，我們提出以雙攝影機系統為平台的性別辨識系統，全景攝影機擷取姿態影影像，PTZ攝影機則擷取人臉影像，並以我們提出的區域紋理方法LBDP(Local Block Difference Pattern) 擷取姿態與人臉的特徵進行性別辨識，如此結合姿態與人臉資訊的方式，可以提高性別辨識的正確率。
我們提出的區域紋理特徵除了繼承LBP計算簡單的優點，而且為了加強其描述物體的能力，我們採用區塊衡量相關程度的概念取代LBP單純只使用中心點像素來計算描述物體的缺點，由於此計算機制的改變，可以更進一步有效的減少像素變化的影響，我們編碼的方式有別於LBP。而在實際描述物體上則是類似LBP，將影像區分成相同大小但是不重疊區塊，將每個區塊在經過LBDP擷取特徵之後，利用統計方式將其轉為histogram，最後將這些histogram連接成特徵向量以描述此物體。
為了提升演算法和系統的穩定性，我們先以姿態影像為基礎做性別辨識來輔助之後階段的人臉性別辨識，其中擷取姿態和人臉特徵擷取部分都是以LBDP區域紋理特徵來達成，而最後的實驗結果，辨識率都能顯示出我們系統有顯著的效果。
摘要(英) Recently, pattern recognition has been widely applied in many areas including ETC (electronic toll collection) system and vision-based access control system. ETC system not only uses RFID sensor but also combines license plate detection and recognition to ensure system’s stability. As to access control system, it needs to extract biometric features such as face, palm print and iris information for authentication. The most critical issue inherent in these applications is the extraction of discriminative features. One of the well-known feature extraction algorithms is Local Binary Pattern (LBP). In the past, many researchers developed local texture descriptors aim at inheriting the advantages of LBP.
In this dissertation, a novel gender classification scheme operating on dual cameras is proposed. The fix camera is used to extract gait image sequences for gait based gender classification and the PTZ camera is used to extract face images for face based gender classification. The classification features of both gait and face are described by the proposed novel texture descriptor called Local Block Difference Pattern (LBDP).
In conventional LBP, the problem of sensitivity to intensity change usually constrains its practicality due to its pixel-based comparison in the encoding mechanism. Different from LBP, the proposed LBDP describes the local texture information by extending the encoding mechanism from pixel-based comparison to block-based comparison so as to extracting more detailed information. The discrimination capability of LBDP is thus enhanced because the difference of local structures and the correlation degree of neighboring blocks are both considered in the proposed encoding mechanism. Moreover, the proposed LBDP can decrease the influence resulting from intensity change because of the expanding of encoding range. To obtain the image descriptor, we first convert the LBDP of each pixel in an image into a decimal value and divide the image into non-overlapping sub-regions. Form a histogram of each sub-region by accumulating the decimal values of all pixels in the sub-region. An image descriptor can then be obtained by concatenating the histograms of all sub-regions to be used as the feature for gender classification.
To improve the performance and stability of the system, we adopt the result generated in gait-based gender classification to assist facial gender classification. The proposed LBDP is utilized in extracting both the gait and facial features. Experimental results verify the validity and excel performance of the proposed system in gender classification.
關鍵字(中) ★ 局部紋理特徵
★ 性別辨識
★ 姿態影像		 關鍵字(英)
論文目次 摘要 I
Abstract II
Chapter 1 : Introduction 1
1.1 Motivation 1
1.2 Organization of Thesis 4
Chapter 2 : Review of Related Work 5
2.1 Local texture descriptors 5
2.1.1 Local Binary Pattern (LBP) 5
2.1.2 Local Directional Pattern (LDiP) 6
2.1.3 Local Derivative Pattern (LDeP) 9
2.1.4 Multi-Block Local Binary Pattern (MB-LBP) 11
2.2 Gait-based gender classification 12
2.3 Face-based gender classification 15
Chapter 3: The Proposed Local Block Difference Pattern 18
3.1 Local Block Different Pattern 18
3.2 Discussion of LBDP 24
Chapter 4 : Framework of Gender classification 28
4.1 Dual mode gender classification 28
4.2 Gait-based gender classification 32
4.3 Face-based gender classification 39
Chapter 5 : Experimental Results 43
5.1 Gait-based gender classification 43
5.2 Face-based gender classification 53
5.3 Dual mode gender classification 56
Chapter 6 : Conclusions and Future Works 60
References 61
參考文獻 [1] J.Bekios-Calfa, J.M.Buenaposada and L.Baumela, “Revisiting Linear Discriminant Techniques in Gender Recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.33, No.4, pp. 858-864, 2011.
[2] B. Moghaddam and M.-H. Yang, “Learning Gender with Support Faces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 5, pp. 707-711, May 2002.
[3] S. Baluja and H.A. Rowley, “Boosting Sex Identification Performance,” International Journal of Computer Vision, Vol. 71, No. 1, pp. 111-119, 2007.
[4] E. Ma ¨kinen and R. Raisamo, “Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 3, pp. 541-547, Mar. 2008.
[5] S.A. Khan, M. Nazir,S. Akram and N. Riaz, “Gender classification using Image Processing Techniques: A Survey,” in Proceedings of International Conference on Mul-titopic, pp 25-30,2011.
[6] T. Ojala, M. Pietikäinen, and D. Harwood, "A Comparative Study of Texture Measures with Classification Based on Feature Distributions", Pattern Recognition, Vol. 29, pp. 51-59, 1996.
[7] T. Jabid, M. H. Kabir, and O. Chae, “Gender classification using local directional pattern,” in Proceedings of the 12th International Conference on Pattern Recognition, 2010.
[8] B. Zhang, Y. Gao, S. Zhao and J. Liu, “Local Derivative Pattern Versus Local Bi-nary Pattern: Face Recognition With High-Order Local Pattern Descriptor,” IEEE Transactions on Image Processing, Vol.19, No.2, pp.533-544, 2010.
[9] L. Zhang, R. Chu, S. Xiang, and S. Z. Li, “Face detection based on Multi-Block LBP representation,” in Proceedings of the International Conference on Biometrics, pp. 11–18, 2007.
[10] M. Pietikäinen and G. Zhao, “Local texture descriptors in computer vision,” in Proceedings of International Conference on Computer Vision, 2009.
[11] T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multi-resolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 7, pp. 971-987, 2002.
[12] L. T. Kozlowski and J. E. Cutting, “Recognizing the sex of a walker from a dynamic point-light display,” Perception and Psyckophysics, Vol. 21, pp. 575-580, 1977.
[13] L. Lee and W. E. L. Grimson, “Gait analysis for recognition and classification,” in Proceeding 5th IEEE International Conference On Automatic Face and Gesture Recognition, Washington, DC, pp. 155-162, 2002.
[14] G. Huang and Y. Wang, “Gender classification based on fusion of multi-view gait sequences,” in Proceeding 8th Asian Conference On Computer Vision, pp. 462-471, 2007.
[15] X. Li, S. J. Maybank, S. Yan, D. Tao, and D. Xu, “Gait components and their application to gender recognition,” IEEE Transactions on System, Man and Cybernetics – part C, Vol. 38, No. 2, pp. 145-155, 2008.
[16] S. Yu, T. Tan, K. Huang, K. Jia, and X. Wu, “A study on gait-based gender classification,” IEEE Transactions on Image Processing, Vol. 18, No. 8, pp. 1905-1910, 2009.
[17] M. Hu, Y. Wang,Z. Zhang and D. Zhang, “Gait-Based Gender Classification Using Conditional Random Field,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.41, No.5, pp. 1429-1439, 2011.
[18] S. Mozaffari, H. Behravan, and R. Akbari, “Gender classification using single frontal image per person: combination of appearance and geometric based features,” International Conference on Pattern Recognition, pp. 1192-1195, 2010.
[19] H.-C. Lian and B.-L. Lu, “Multi-view gender classification using local binary patterns and support vector machines,” International Symposium on Neural Networks, pp. 202-209, 2006.
[20] C. Shan, “Learning local binary patterns for gender classification on real-world face images,” Pattern Recognition Letters, pp. 431-437, 2012.
[21] B. Li, X.-C. Lian, and B.-L. Lu, “Gender classification by combining clothing, hair and facial component classifiers,” Neurocomputing, pp. 18-27, 2012.
[22] B. Xia, H. Sun, and B.-L. Lu, “Multi-view gender classification based on local gabor binary mapping pattern and support vector machines,” International Joint Conference on Neural Networks, pp. 3388-3395, 2008.
[23] J.-M. Fellous, “Gender discrimination and prediction on the basis of facial metric information,” Vision Research, pp. 1961-1973, 1997.
[24] P. Zeng, Y. Zhang, F. Duanb, “Gender Classification System in Uncontrolled Environments,” Intelligent Robots and Computer Vision, 2011.
[25] M. Toews, and T. Arbel, “Detection, Localization, and Sex Classification of Faces from Arbitrary Viewpoints and under Occlusion,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.31, No. 9, pp. 1567-1581, 2009.
[26] L. A. Alexandre, “Gender recognition: a multiscale decision fusion approach,” Pattern Recognition Letters, pp. 1422-1427, 2010.
[27] T. Ahonen, A. Hadid, and M. Pietik¨ainen, “Face description with local binary patterns: Application to face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 12, pp. 2037–2041, 2006.
[28] D. Huang, C. Shan, M. Ardabilian, Y. Wang and L. Chen, “Local Binary Patterns and Its Application to Facial Image Analysis: A Survey,” IEEE Transactions on System, Man and Cybernetics – part C : Applications and Reviews,Vol.41,No.6, pp. 765 – 781,2011.
[29] P. Viola and M. Jones, “Robust real-time face detection,” International Journal of Computer Vision, Vol. 57, No.2, pp. 137–154, 2004.
[30] 21. P. S. Liao, T. S. Chen, and P. C. Chung, “A fast algorithm for multilevel thresholding,” Journal of Information Science and Engineering, Vol. 17, pp. 713-737, 2001.
[31] L. Wang, H. Ning, T. Tan, and W. Hu, “Fusion of static and dynamic body biometrics for gait recognition,” IEEE Transactions on Circuits And Systems For Video Technology, Vol. 14, No. 2, pp. 149-158, 2004.
[32] J. Han and B. Bhanu, “Individual recognition using gait energy image,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 2, pp. 316-322, 2006.
[33] A. F. Bobick and J. W. Davis, “The recognition of human movement using temporal templates,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, No. 3, pp. 257-267, 2001.
[34] S. Sarkar, P. J. Phillips, Z. Liu, I. R. Vega, P. Grother, and K. W. Bowyer, “The human id gait challenge problem: Data sets, performance, and analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 2, pp. 162-177, 2005.
[35] Q. Ma, S. Wang, D. Nie, and J. Qiu, “Recognizing humans based on gait moment image,” in Proceeding 8th ACIS International Conference on Software Engineering, Artificial Intelligent. Networking, and Parallel/Distributed Computing, 2007.
[36] V. Vapnik, “Structure of statistical learning theory,” Computational Learning and Probabilistic Reasoning, pp. 3-31, 1996.
[37] T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, “Active shape models - their training and application,” Computer Vision and Image Understanding, pp. 38-59, 1995.
[38] M. Hu, Y. Wang, Z. Zhang, and Y. Wang, “Combining spatial and temporal information for gait based gender classification,” in Proceedings of International Conference on Pattern Recognition, pp. 3679–3682, 2010.
指導教授 范國清(Kuo-chin Fan) 審核日期 2014-7-30
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題，請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407，或E-mail聯絡  - 隱私權政策聲明