請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/44625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 | Huang-Ming Chang | en |
| dc.contributor.author | 張晃銘 | zh_TW |
| dc.date.accessioned | 2021-06-15T03:51:55Z | - |
| dc.date.available | 2010-07-21 | |
| dc.date.copyright | 2010-07-21 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-07-10 | |
| dc.identifier.citation | [1] M. Turk and A. Pentland (1991), “Face recognition using eigenfaces,” Proc. IEEE Conference on Computer Vision and Pattern Recognition. pp. 586–591.
[2] P. Belhumeur, J. Hespanha, and D. Kriegman (july 1997), “Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection,” IEEE Transactions on pattern analysis and machine intelligence 19 (7): 711. [3] Ahonen, T., Hadid, A., and Pietikainen, M. 2006, “Face Description with Local Binary Patterns: Application to Face Recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 28, 12 (Dec. 2006), 2037-2041. [4] Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., and Ma, Y. 2009, “Robust Face Recognition via Sparse Representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 2 (Feb. 2009), 210-227. [5] Zhang, W., Shan, S., Gao, W., Chen, X., and Zhang, H. 2005, ”Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition,” In Proceedings of the Tenth IEEE international Conference on Computer Vision (Iccv'05) Volume 1 - Volume 01 (October 17 - 20, 2005). ICCV. IEEE Computer Society, Washington, DC, 786-791. [6] Gang Hua and Amir Akbarzadeh, 'A Robust Elastic and Partial Matching Metric for Face Recognition,' In Proc. IEEE 12th International Conf. on Computer Vision (ICCV'2009), Kyoto, Japan, October, 2009. [7] S. A. J. Winder and M. Brown, “Learning local image descriptors,” In CVPR, 2007. [8] Simon Winder, Gang Hua, and Matthew Brown, 'Picking the Best DAISY,' Com-puter Vision and Pattern Recognition, IEEE Computer Society Conference on, vol. 0, pp. 178-185, 2009. [9] Haitao Wang, Li, S.Z. and Yangsheng Wang, 'Face recognition under varying light-ing conditions using self quotient image,' Automatic Face and Gesture Recognition, 2004. Proceedings. Sixth IEEE International Conference on , vol., no., pp. 819- 824, 17-19 May 2004 [10] Lowe, D. G. 2004, “Distinctive Image Features from Scale-Invariant Keypoints,” Int. J. Comput. Vision 60, 2 (Nov. 2004), 91-110. [11] Krystian Mikolajczyk, Cordelia Schmid, 'A Performance Evaluation of Local De-scriptors,' IEEE Trans. Pattern Anal. Mach. Intell. 27, 10 (Oct. 2005), 1615-1630. [12] Wikipedia, “Eigenface,” http://en.wikipedia.org/wiki/Eigenface. [13] P. J. Phillips, P. J. Rauss, and S. Z. Der, ' FERET (Face Recognition Technology) Recognition Algorithm Development and Test Results,' October 1996. Army Re-search Lab technical report 995. [14] Freeman, W. T. and Adelson, E. H. 1991, “The Design and Use of Steerable Filters,” IEEE Trans. Pattern Anal. Mach. Intell. 13, 9 (Sep. 1991), 891-906. [15] Zhang, B., Gao, Y., Zhao, S., and Liu, J. 2010, “Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor,” Trans. Img. Proc. 19, 2 (Feb. 2010), 533-544. [16] S. Johnson, 'Hierarchical clustering schemes,' Psychometrika, vol. 32, no. 3, pp. 241-254, September 1967. [17] J. B. Macqueen, 'Some methods for classification and analysis of multivariate ob-servations,' in Procedings of the Fifth Berkeley Symposium on Math, Statistics, and Probability, vol. 1. University of California Press, 1967, pp. 281-297. [18] Pei-Ruu Shih, “Face Recognition with Local Binary Patterns and Partial Matching,” M.S. thesis, GINM, NTU. [19] Kuan-Ting Liu, “Face Representation and Recognition based on Texture Scale and Orientation through Gabor Filter,” M.S. thesis, GINM, NTU. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/44625 | - |
| dc.description.abstract | 人臉辨識一直是電腦視覺領域中最重要的議題之一。經過數十年的研究，在光線以及臉部表情受到控制的情形下，對於人的正臉，其辨識率超過99%。但在光線、臉部表情、人臉的姿勢有所變化，或是人臉受到遮蔽時，其辨識率就會大幅下降。為了解決這些問題，在本文中以影像描述子(Local Image Descriptor)描述人臉，並且以部分比對(Partial Matching)的方式比對兩張人臉照片。在本文中，我們使用了各種不同的影像描述子，並且試著找出最好的一個。為了提升計算效率，我們將部分的運算平行化，並且實作在多核心的系統上。我們將重點放在非監督式學習(Unsupervised Learning)上，在第一組測試資料的309張人臉中，將其分類為100組時，準確率為99.25%。在第二組測試資料的838張人臉中，將其分類為253個群組時，準確率為99.82%。此結果與Google Picasa單機版本的結果相當相似。但我們的速度仍慢了近8倍以上，故在加速計算上仍有進步的空間。 | zh_TW |
| dc.description.abstract | Face recognition is an important topic in computer vision in the past decades. The rec-ognition rate of frontal faces now is higher than 99% if lighting and facial expressions are controlled. However, if the lighting, facial expression, and pose are various, or the face is under partial occlusion, the recognition rate becomes much lower. In this paper, following Hua and Akbarzadeh 2009’s approach, we implement face representation us-ing local image descriptor, and compare two faces by partial matching. We try kinds of local image descriptors to find the best one. To improve our performance, we parallelize some parts of our computation, and implement it in a quad-core system. In our first da-taset with 309 faces of 5 subjects, we get a recognition precision of 99.25% with 100 clusters; and in our second dataset with 838 faces of 8 subjects, we get a recognition precision of 99.82% with 253 clusters. These results are similar to that of Google Picasa PC version, however, ours is currently at least 8 times slower. Further speedup is ex-pected in the future work. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T03:51:55Z (GMT). No. of bitstreams: 1 ntu-99-R97922065-1.pdf: 2256582 bytes, checksum: 74f73c42b5fc28a846cefb36ed412db7 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 致謝 i
摘要 ii Abstract iii Contents iv List of Figures vii List of Tables ix 1 Introduction 1 2 Related Work 4 2.1 Face Recognition 4 2.2 Local Image Descriptors 9 2.3 Cluster analysis 10 3 System Overview 13 3.1 Face Representation 13 3.2 Partial Matching Metric 16 3.3 Local Image Descriptors 18 3.3.1 Transformation block (T-block) 18 3.3.2 Spatial Block (S-block) 21 3.3.3 Post Normalization (N-block) 23 3.4 Multi-core Programming 23 4 Experiments and Results 24 4.1 Face datasets 24 4.4.1 The FERET face database 24 4.4.2 Home photo datasets 25 4.2 Settings 26 4.3 Descriptor Arguments 26 4.4 Interchanging T-Block 26 4.4.1 Supervised Learning 27 4.4.2 Unsupervised Learning 28 4.5 Interchanging S-Block 35 4.5.1 Supervised Learning 35 4.5.2 Unsupervised Learning 37 4.5 Comparison with Google Picasa (PC and web version) 42 5 Conclusion and Future Work 44 Bibliography 45 Appendix I: Examples of FERET database 48 Appendix II: Examples of HomePhoto_set01 49 Appendix III: Examples of HomePhoto_set02 50 Resume 51 | |
| dc.language.iso | en | |
| dc.subject | 部分比對 | zh_TW |
| dc.subject | 平行運算 | zh_TW |
| dc.subject | 人臉辨識 | zh_TW |
| dc.subject | 影像描述子 | zh_TW |
| dc.subject | Face Recognition | en |
| dc.subject | Multithread | en |
| dc.subject | Partial Matching | en |
| dc.subject | Local Image Descriptor | en |
| dc.title | 以影像描述子為基礎之人臉辨識 | zh_TW |
| dc.title | Face Recognition Based on Local Image Descriptor | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 洪一平(Yi-Ping Hung),楊傳凱(Chuan-kai Yang) | |
| dc.subject.keyword | 人臉辨識,影像描述子,部分比對,平行運算, | zh_TW |
| dc.subject.keyword | Face Recognition,Local Image Descriptor,Partial Matching,Multithread, | en |
| dc.relation.page | 51 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-07-12 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
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