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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 顏嗣鈞(Hsu-Chun Yen) | |
| dc.contributor.author | Jun-Li Lu | en |
| dc.contributor.author | 盧俊利 | zh_TW |
| dc.date.accessioned | 2021-06-15T06:21:52Z | - |
| dc.date.available | 2015-08-12 | |
| dc.date.copyright | 2010-08-12 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-08-10 | |
| dc.identifier.citation | [1] M. Turk, A. Pentland, 'Face recognition using Eigenfaces, 'IEEE Conference on Computer Vision and Pattern Recognition, pp. 586-591, 1991.
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Poggio, 'Face Recognition: Feature versus Templates,' IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol.15, No. 10, 1993. [8] B. Heisele, T. Serre, and T. Poggio, 'A component-based framework for face detection and identification,' International Journal of Computer Vision, Vol. 74, No 2, Aug. 2007. [9] R. Duda, P. Hart, and D. Stork, 'Pattern Classification,' 2nd ed. John Wiley & Sons, 2001. [10] J. Ho, M. Yang, J. Lim, K. Lee, and D. Kriegman, 'Clustering appearances of objects under varying illumination conditions,' IEEE International Conference on Computer Vision and Pattern Recognition, pp. 11–18, 2003. [11] J. Wright, A. Ganesh, A. Yang, and Y. Ma, 'Robust face recognition via sparse representation,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 31, No. 2, pp. 210-227, Feb. 2009. [12] K. Lee, J. Ho, and D. 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'Surf-face: Face recognition under viewpoint consistency constraints,' British Machine Vision Conference, Sept. 2009. [18] T. Serre, M. Kouh, C. Cadieu, U. Knoblich, G. Kreiman, T. Poggio, 'A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex,' Computer Science and Artificial Intelligence Laboratory Technical Report, December 19, 2005. [19] K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. V. Gool, 'A comparison of affine region detectors,' International Journal of Computer Vision, Vol. 65, No. 1-2, Nov. 2005. [20] P. Viola, and M. Jones, 'Robust real-time face detection,' International Journal of Computer Vision, Vol. 57, No. 2, May 2004. [21] Y. Freund and R. Schapire, 'A Decision-theoretic Generalization of On-line Learning and an Application to Boosting,' Journal of Computer and System Sciences, 1997. [22] P. Viola, and M. Jones, 'Rapid object detection using boosted cascade of simple features,' IEEE Conference on Computer Vision and Pattern Recognition, 2001. [23] Luxand-Detect and Recognize Faces with Luxand FaceSDK. http://www.luxand.com/facesdk/ [24] Independent JPEG Group, an informal group that writes and distributes a widely used free library for JPEG image compression. http://www.ijg.org/ [25] Libpng, the official PNG reference library. http://www.libpng.org/pub/png/libpng.html [26] The EasyBMP Project. http://easybmp.sourceforge.net [27] G. Dorko, and C. Schmid, 'Object class recognition using discriminative local features,' Technical report, INRIA Grenoble, Sept. 2005. [28] A. Majumdar, and R. Ward, 'Discriminative SIFT Features for Face Recognition, ' Canadian Conference on Electrical and Computer Engineering, pp. 27–30, 2009. [29] K. Mikolajczyk, and C. Schmid, 'A performance evaluation of local descriptors,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 10, pp. 1615-1630, Oct. 2005. [30] P. Phillips, H. Moon, P. Rauss, and S. Rizvi, 'The FERET evaluation methodology for face recognition algorithms,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 10, October 2000. [31] V. Blanz, and T. Vetter, 'Face recognition based on fitting a 3D morphable model,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 9, pp. 1063-1074, Sept. 2003. [32] J. Hartigan, and M. Wong, 'A k-means clustering algorithm,' Journal of the Royal Statistical Society, Series C (Applied Statistics), Vol. 28, pp. 100-108, 1979. [33] M. Buhmann, 'Radial Basis Functions: Theory and Implementations,' ISBN 978-0-521-63338-3. [34] A. Martinez, and R. Benavente, 'The AR Face Database,' CVC Technical Report No. 24, 1998. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47842 | - |
| dc.description.abstract | The task of recognizing human faces from frontal views with expressions, illumination changes, and occlusions had been in investigated deeply by many proposed algorithms. However, few researches are focused on the problem of recognizing human faces with varying pose angles. For this problem, based on the usage of local descriptors, we propose a face recognition system that mainly consists of weighting face subjects from a feature’s view and consideration to the deformation degree between faces. For weighting face subjects from a feature’s view, it provides a more precise matching in local descriptors than Nearest NNNDR, a popular matching strategy. Considering the deformation degree between faces gives a new insight into faces with varying pose angles. In the recognition system, we use the technique of facial features localization to assist in finding the vicinity of a feature and measuring the deformation degree between faces.
The face recognition system is experimented on the AR database and the FERET database. We use the support rate of the answer subject, the rate of no face detection, and the recognition rate to observe the behavior of our system. In the experiments, the support rate of the answer subject increases significantly and a correlation between these three indices is found. The recognition rate of our method is 97.49% for faces with a pose angle within ±40 degrees and without occlusions. We also discuss the impact of occlude faces with varying pose angles. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T06:21:52Z (GMT). No. of bitstreams: 1 ntu-99-R97921065-1.pdf: 6233166 bytes, checksum: 017b7e89a0d23ef967f695fd770cbf3f (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES ix Chapter 1 Introduction 1 Chapter 2 Related Work 5 2.1 Scale Invariant Feature Transform 5 2.2 Feature Extraction Methods 7 2.3 Known Matching Strategies 8 2.4 Face Detection 9 2.5 Facial Features Localization 10 Chapter 3 Face Recognition Using Local Feature Voting 12 3.1 Motivations and Ideas 12 3.2 Feature Clustering 17 3.3 Face or Facial Features Localization 19 3.4 Vicinity-Based Matching 20 3.5 Local Feature Voting 22 Chapter 4 Experimental Results 25 4.1 Comparative metrics 25 4.2 Comparative Issues 26 4.3 Face Databases 27 4.4 Conditions and Results 27 4.4.1 The Average Support Rate of the Answer Subject 28 4.4.2 Frontal Faces 29 4.4.3 Faces with Varying Pose Angles 30 4.4.4 The Impact of Occlusions 33 Chapter 5 Conclusion and Future Work 36 REFERENCES 38 | |
| dc.language.iso | en | |
| dc.subject | 人臉辨識 | zh_TW |
| dc.subject | 形變程度 | zh_TW |
| dc.subject | 加速強健特徵點 | zh_TW |
| dc.subject | deformation degreel | en |
| dc.subject | face recognition | en |
| dc.subject | SURF | en |
| dc.title | 利用局部特徵點票決方法於人臉辨識 | zh_TW |
| dc.title | Face Recognition Using Local Feature Voting | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 雷欽隆(Chin-Laung Lei),莊仁輝(Jen-Hui Chuang),郭斯彥(Sy-Yen Kuo) | |
| dc.subject.keyword | 人臉辨識,加速強健特徵點,形變程度, | zh_TW |
| dc.subject.keyword | face recognition,SURF,deformation degreel, | en |
| dc.relation.page | 41 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-08-10 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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