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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46612完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 歐陽明(Ming Ouhyoung) | |
| dc.contributor.author | Pei-Ruu Shih | en |
| dc.contributor.author | 施佩汝 | zh_TW |
| dc.date.accessioned | 2021-06-15T05:18:44Z | - |
| dc.date.available | 2010-07-21 | |
| dc.date.copyright | 2010-07-21 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-07-20 | |
| dc.identifier.citation | [1]
A.M. Martinez, R. Benavente. The AR Face Database. CVC Technical Report #24, June 1998. [2] Andrew Wagner, Hohn Wright, Arvind Ganesh, Zihan Zhou, and Yi Ma. Towards a Practical Face Recognition System: Robust registration and Illumination by Sparse Representation. In IEEE International Conference on Computer Vision and Pattern Recognition, 2009. [3] Baochang Zhang, Yongsheng Gao. Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor. In IEEE Transactions on Image Processing, VOL. 19, No. 2, February 2010. [4] Carlos D. Castillo, David W. Jacobs. Using Stereo Matching with General Epipolar Geometry for 2D face Recognition across Pose. In IEEE Transaction on Pattern Analysis and Machine Intelligence, VOL. 31, No. 12, December 2009. [5] Che-Hua Yeh, Pei-Ruu Shih, Kuan-Ting Liu, Yin-Tzu Lin, Huang-Ming Chang, Ming Ouhyoung. A Comparison of Three Methods of Face Recognition for Home Photos. ACM SIGGRAPH Poster, August 2009. [6] Daniel González-Jiménez, and Luis Alba-Castro. Toward Pose-Invariant 2-D Face Recognition Through Point Distribution Models and Facial Symmetry. In IEEE Transactions on Information Forensics and Security, VOL. 2, No. 3, September 2007. [7] Gang Hua, Amir Akbarzadeh. A Robust Elastic and Partial Matching Metric for Face Recognition. In Proc. ICCV, 2009 [8] Hyeonjoon. Moon, and P.J. Phillips, Computational and Performance aspects of PCA-based Face Recognition Algorithms, Perception, Vol. 30, 2001, pp. 303-321 [9] John Wright, Allen Y. Yang, Arvind Ganesh, Shankar Sastry, Yi Ma. Robust Face Recognition via Sparse Representation. IEEE Transactions on Pattern Analysis and Machine Intelligence. VOL. 31, No. 2, February 2009. [10] Junzhou Huang, Xiaolei Huang, and Dimitris Metaxas. Simultaneous Image Transformation and Sparse Representation Recovery. IEEE International Conference on Computer Vision and Pattern Recognition, 2008. [11] Nguyen Xuan Vinh, Epps, J. and Bailey, J., Information Theoretic Measures for Clusterings Comparison: Is a Correction for Chance Necessary?, in Procs. the 26th International Conference on Machine Learning, 2009. [12] Nicolas Gourier, Jérôme Naisonnasse, Daniela Hall, and James L. Corwley. Head Pose Estimation on Low Resolution Images. In: Stiefelhagen, R., Garofolo, J.S. (eds.) CLEAR 2006. LNCS, vol. 4122, Springer, Heidelberg (2007) [13] P. Jonathon. Phillips. Support vector machines applied to face recognition. In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, NIPS’98, 1998. [14] Stephen C. Johnson. Hierarchical clustering schemes. In Psychometrika. Volume 32, Number 3. Sep. 1976. [15] Strehl, A., & Ghosh, J. Cluster ensembles - a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583{617}, 2002. [16] T Ojala, M Pietikäinen, T. Mäenpää. Multiresolution Gray-scale and Rotation Invariant Texture Classification with Local Binary Patterns. In IEEE Transactions on Pattern Analysis and Machine Intelligence 2002, 24(7):971-987. [17] T. Ahonen, A. Hadid, and M. Pietikainen. Face Recognition with Local Binary Patterns. In Proc. ECCV, 2004. [18] Weenchao Zhang, Shiguang Shan, Wen Gao, Xilin Chen, Hongming Zhang. Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition. In Proc. ICCV, 2005. [19] William M. Rand. Objective Criteria for the Evaluation of Clustering Methods. In Journal of the American Statistical Association. 1971. [20] Xiaoyang Tan, and Bill Triggs. Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions. AMFG 2007, LNCS 4778, pp. 168-182, 1007. Springer-Verlag Berlin Heidelberg 2007. [21] Xiaozheng Zhang, and Yongsheng Gao. Face Recognition Across Pose: A Review. In Pattern Recognition, Volume 42, Issue 11, November 2009. [22] Xiujuan Chai, Shiguang Shan, Xilin Chen, and Wen Gao. Local Linear Regression (LLR) for Pose Invariant Face Recognition. In IEEE Transaction of Image Processing 2007. [23] Yi Zhou, Lie Gu, Hong-Jiang Zhang. Bayesian Tangent Shape Model: Estimating Shape and Pose Parameters via Bayesian Inference. IEEE International Conference on Computer Vision and Pattern Recognition, Wisconsin, June 2003. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46612 | - |
| dc.description.abstract | 隨著數位相機的普及，人們出門遊玩時，總會拍攝許多照片。我們認為辨認出誰在這些照片中是件有意義且有趣的事。因此與傳統的人臉辨識不同，我們著重的地方在於我們處理的是一般大眾出遊拍的照片。這些照片可能會導致人臉上有不同的光影變化，人臉可能不是正對攝影機，或是臉上有頭髮墨鏡等遮掩五官的物品。這使得我們想要解決的問題無法單純使用傳統方式解決。因此，在這篇論文中，我們使用區域二元圖樣(Local Binary Patterns)及自行提出的部份比對(Partial Matching)的方式來實做人臉辨識。在結果的評估上，我們使用「AR」及「FERET」的正臉資料當作評估基礎，另外加入兩組實驗室成員出遊所拍攝的照片當作最後的評估結果。在資料集一(Dataset I)的309張照片中，我們的系統在100 個群組時，可以達到99.46%的正確率，且Google線上版(Online Version)有94個群組正確率在99.92%的，而單機版有99個群組，正確率可達到100%。而在資料集二(Dataset II)的838張照片中，若將照片分成253個群組，我們可以達到99.57%的正確率，而Google線上版有195個群組正確率為99.49%，單機版有253個群組，正確率為100%。為了執行效能，我們將系統實做在四核心(quad-core)系統上，並將部分工作平行化處理。在我們309張生活照的實驗中，使用單一執行緒需要73分鐘，但使用四個執行緒只需24分鐘。因此我們約有3倍的效能加速。 | zh_TW |
| dc.description.abstract | Due to the popularity of digital cameras, when people go on vacation, they will take many pictures. We think it is very meaningful and interesting to identify who are in these pictures. Therefore, different from traditional face recognition problem, we focus on those pictures taken by everyday people. These pictures may have different illumination, different poses, or partially occlusion, which will lead to significant performance dropping using traditional face recognition algorithm. Therefore, in this paper, we present a novel algorithm based on Local Binary Patterns and then combined with Partial Matching. In result evaluation, we will use the AR dataset, FERET dataset, and two home-photo datasets. In addition, we will compare with Google Picasa, which is almost the industry standard, and our performance is no worse than the performance of Google Picasa is using two home-photo datasets. In our system, we get the precision 99.46% in the home photo dataset I (309 images) with 100 clusters, and Picasa will get 99.92% precision with 94 clusters in web version and 100% precision with 99 clusters in download version. In addition, we will get the precision 99.59% in the home photo dataset II (838 images) from 253 images, and Picasa will get 99.49% precision with 190 clusters in web version and 100% precision with 253 clusters in download version. Moreover, we implement the system in a quad-core system, and also implement certain parts of our system in parallel. In our experiment, if we use only a single thread in our system, the executing time of 309 images is 73 minutes. However, if we use four threads in our quad-core PC, we can finish the same job in 24 minutes. It is almost three times faster than single-thread. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T05:18:44Z (GMT). No. of bitstreams: 1 ntu-99-R97944003-1.pdf: 2307527 bytes, checksum: e2833a304b9692a8acde3ce0e4bc9f1d (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 摘要 I
Abstract II Table of Contents IV List of Figures VII Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Problem and Proposed Solution 2 1.3 Thesis Organization 5 Chapter 2 Related Works 6 2.1 Algorithms for Face Recognition 6 2.2 Local Binary Pattern 7 2.3 Local Image Descriptor and Partial Matching 10 Chapter 3 Implementation 12 3.1 Local Binary Pattern 12 3.1.1 Local binary pattern and its extension 12 3.2 Local Derivative Pattern 16 3.2.1 Second-order Local Derivative Pattern 16 3.2.2 Nth-order Directional Local Derivative Pattern 19 3.2.3 Histogram 21 3.2.4 Compete with LBP 22 3.3 Facial Descriptor 22 3.3.1 Face Description with LBP 22 3.3.2 Spatial Block 24 3.3.3 Facial Descriptor 25 3.4 Similarity Measurement 26 3.4.1 Partial Matching Metric 26 3.4.2 Partial Matching with LBP 28 3.4.3 Advantage of Partial Matching 29 3.5 Clustering 29 3.5.1 Nearest Neighbor 29 3.5.2 KNN 30 3.5.3 Complete-Linkage Clustering 31 3.6 Performance Optimization 34 Chapter 4 Experiment 36 4.1 Data sets 36 4.2 Supervised Learning 37 4.3 Unsupervised Learning 40 Chapter 5 Conclusion 48 5.1 Discussion 48 5.2 Discussion 49 5.3 Future Work 50 Bibliographic 51 Appendix I Examples of AR Datasets 53 Appendix II Examples of FERET Datasets 54 Appendix III Examples of Home Photo Dataset I 55 Appendix IV Examples of Home Photo Dataset II 56 Resume 57 | |
| dc.language.iso | en | |
| dc.subject | 部分比對 | zh_TW |
| dc.subject | 區域二元圖樣 | zh_TW |
| dc.subject | 人臉辨識 | zh_TW |
| dc.subject | 平行計算 | zh_TW |
| dc.subject | Partial Matching | en |
| dc.subject | Multithreads | en |
| dc.subject | Local Binary Patterns | en |
| dc.subject | Face recognition | en |
| dc.title | 以區域二元圖樣與部分比對為基礎之人臉辨識 | zh_TW |
| dc.title | Face Recognition with Local Binary Patterns and Partial Matching | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 洪一平(Yi-Ping Hung),徐宏民(Winston H. Hsu) | |
| dc.subject.keyword | 人臉辨識,區域二元圖樣,部分比對,平行計算, | zh_TW |
| dc.subject.keyword | Face recognition,Local Binary Patterns,Partial Matching,Multithreads, | en |
| dc.relation.page | 57 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-07-21 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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