請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/30633
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 洪一平(Yi-Ping Hung) | |
dc.contributor.author | Yun-Wen Wang | en |
dc.contributor.author | 王韻雯 | zh_TW |
dc.date.accessioned | 2021-06-13T02:10:45Z | - |
dc.date.available | 2007-07-03 | |
dc.date.copyright | 2007-07-03 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-06-25 | |
dc.identifier.citation | [1] P. Belhumeur, J. Hespanha, and D. Kriegman. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711–720, 1997.
[2] J. W. Bernd Heisele, Purdy Ho and T. Poggio. Face recognition: componentbased versus global approaches. Computer Vision and Image Understanding, 91(1):6–12, 2003. [3] D. Beymer and T. Poggio. Face recognition from one example view. In Proceedings of the Fifth IEEE International Conference on Computer Vision, pages 500–507, 1995. [4] R. Brunelli and T. Poggio. Face recognition: Features versus templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10):1042–1052, 1993. [5] T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active apperance models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(6):681–685, 2001. [6] S. Eickeler, S. Muller, and G. Rigoll. High performance face recognition using pseudo 2-d hidden markov models. European Control Conference, 1999. [7] Y. Fang, T. Tan, and Y. Wang. Fusion of global and local features for face verification.In International Conference on Pattern Recognition, pages 382–385,2002. [8] M. J. Jones and P. Viola. Face recognition using boosted local features. Technical Report TR 2003-25, Mitsubishi Electric Research Laboratory, 2003. [9] D.-H. Kim, J.-Y. Lee, J. Soh, and Y.-K. Chung. Real-time face verification using multiple feature combination and a support vector machine supervisor. In International Conference on Acoustics, Speech, and Signal Processing, pages 145–148, 2003. [10] S. Kuo and O. Agazzi. Keyword spotting in poorly printed documents using pseudo 2-d hidden Markovmodels. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(8):842–848, 1994. [11] A. Lanitis, C. J. Taylor, and T. F. Cootes. An automatic face identification system using flexible appearance model. British Machine Vision Conference, 1:65–74, 1994. [12] A. Lanitis, C. J. Taylor, and T. F. Cootes. Automatic interpretation and coding of face images using flexible models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):743–756, 1997. [13] K.-C. Lee, J. Ho, M.-H. Yang, and D. Kriegman. Video-based face recognition using probabilistic appearance manifolds. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages I–313 – I–320, 2003. [14] A. M. Martinez. Recognizing imprecisely localized, partially occluded, and expression variant faces from a single sample per class. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(6):748–763, 2002. [15] A. M. Martinez and A. Kak. PCA versus LDA. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(2):228–233, 2001. [16] K. Messer, J. Kittler, M. Sadeghi, M. Hamouz, A. Kostyn, S. Marcel, S. Bengio, F. Cardinaux, C. Sanderson, N. Poh, and Yann. Face authentication competition on the banca database. In The First International Conference on Biometric Authentication, 2004. [17] B. Moghaddam and A. Pentland. Probabilistic visual learnign for object detection. In ICCV, pages 786–793, 1995. [18] B. Moghaddam and A. Pentland. Probabilistic visual learning for object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):696–710, 1997. [19] B. Moghaddam, W. Wahid, and A. Pentland. Beyond eigenfaces: probabilistic matching for face recognition. Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, pages 30–35, 1998. [20] C. Nastar and M. Mitschke. Real-time face recognition using feature combination. In Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, pages 312–319, 1998. [21] A. V. Nefian and M. Hayes. An embedded HMM-based approach for face detection and recognition. In Acoustics, Speech, and Signal Processing, volume 6, pages 3553–3556, 1999. [22] A. V. Nefian and M. H. Hayes. Face detection and recognitionu sing hidden markov models. In Proceedings of IEEE International Conference on Image Processing, volume 1, pages 141–145, 1998. [23] A. V. Nefian and M. H. Hayes. Hidden markov models for face recognition. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing, pages 2721–2724, 1998. [24] A. Pentland, B. Moghaddam, and T. Starner. View-based and modular eigenspaces for face recognition. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 84–91, 1994. [25] P. J. Phillips, R. M. Mccabe, and R. Chellappa. Biometric image processing and recognition. In Proceedings of European Signal Processing Conference, 1998. [26] M. Pontil and A. Verri. Support vector machines for 3d object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20:637–646, 1998. [27] L. R. Rabiner. A toturial on hidden markov models and selected applications in speech recognition. In Proceedings of the IEEE, pages 257–286, 1989. [28] F. Samaria. Face segmentation for identification using hidden markov models. In British Machine Vision Conference, pages 399–408, 1993. [29] F. Samaria. Face recognition using hidden markov model. PhD thesis, Engineering Department,University of Cambridge, October 1994. [30] T. Sim, S. Baker, and M. Bsat. The cmu pose, illumination, and expression database. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12):1615–1618, 2003. [31] M. Turk and A. Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71–86, 1991. [32] P. Viola and M. Jones. Robust real-time object detection. In International Journal of Computer Vision, volume 57, pages 137–154, 2004. [33] J. Wang, K. N. Plataniotic, and A. N. Venetsanopoulos. Combining features and decisions for face detection. In International Conference on Acoustics, Speech, and Signal Processing, volume 5, pages 717–720, 2004. [34] X. Wang and S. Tang. Using random subspace to combine multiple features for face recognition. In Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pages 284–289, 2004. [35] L. Wiskott, J. Fellous, N. Kruger, and C. von der Malsburg. Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):775–779, 1997. [36] W. Y. Zhao, R. Chellappa, J. P. Phillips, and A. Rosenfeld. Face recognition: A literature survey. Technical Report CAR-TR-948, Center for Automation Research, University of Maryland, 2000. [37] W. Y. Zhao, A. Krishnaswamy, R. Chellappa, D. L. Swets, and J.Weng. Discriminant analysis of principal components for face recognition. In H. Wechsler, P. J. Phillips, V. Bruce, F. Fogelman-Soulie, and T. S. Huang, editors, Face Recognition: From Theory to Applications, volume 163 of NATO ASI Series F, Computer and Systems Sciences, pages 73–85. Springer, 1998. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/30633 | - |
dc.description.abstract | 針對人臉辨識這個問題,本文提出一個從人臉二維影像中擷取人臉特徵方法。此方法即使在頭部轉動變化大時也能夠準確得到人臉特徵的位置。我們利用嵌入隱藏式馬可夫模型來塑造人臉模型,並利用塑造過程的中間產物—狀態分佈序列來擷取長方形的人臉特徵。利用單一嵌入隱藏式馬可夫模型,我們能夠對單一身份固定姿勢下的人臉進行臉部分割、擷取特徵的動作,當頭部轉動變化大時,我們使用人臉影像訓練出多個嵌入隱藏式馬可夫模型,使得我們的方法能夠準確取得
人臉特徵。針對人臉身份識別和人臉身份確認這兩個議題,我們為每一個使用者的每一個特徵建立其外觀子空間,進而利用樣板比對的方法估計特徵相似度。接著再使用Adaboost 演算法將每一個特徵訓練成弱分類器,並合併所有的弱分類器達到結合各特徵的效果。我們利用卡內基美隆大學提供的PIE 人臉資料庫、劍橋大學提供的ORL 人臉資料庫、以及我們實驗室自己拍攝的人臉資料庫上進行人臉辨識的實驗。經由與其他數個方法的比較,我們的方法均獲得較好的辨識率。實驗結果證實在頭部轉動變化大時,我們的方法依然能夠準確的取出人臉特徵,進而在人臉身份比對和人臉身份確認下有較高的準確度。 | zh_TW |
dc.description.abstract | We propose an algorithm for extracting facial features robustly from images for face recognition even under large pose variation. Rectangular facial features are retrieved via the by-products of an embedded Hidden Markov Model (HMM) which decodes an observed face image into a state sequence. While an HMM is able to segment images into features at a fixed pose, multiple HMMs are trained for each individual to robustly extract features under large pose variation. Using the extracted features of each individual, appearance models based on subspaces are constructed for face identification and verification. Then Adaboost is used for feature combination while each weak classifier compared the distance metric of one facial feature. The effectiveness of the proposed approach is validated through empirical studies against numerous methods using the CMU PIE, ORL and our lab’s database. Our experiments demonstrate that the proposed approach is able to extract facial features robustly, thereby rendering superior results in identification and superior performance in verification under large pose variation. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T02:10:45Z (GMT). No. of bitstreams: 1 ntu-96-R94922035-1.pdf: 1679826 bytes, checksum: 3afbf4edee8815fffde10125fa853939 (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | 1 Introduction . . . . . . . . . . . . . . . . . .. . . . . . . . 1
1.1 Overview of Face Recognition . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Face . . . . . . . . . . . . . . Identification . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.2 Face Verification . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Holistic Approach and Local Feature Approach . . . . . . . . . . . . 3 1.3 Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Organization of the thesis . . . . . . . . . . . . . . . . . . . . . . . . 6 2 Relative Work . . . . . . . . . . . . . . . . . .. . . . . . . .7 2.1 Hidden Markov Model . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Embedded Hidden Markov Model . . . . . . . . . . . . . . . . . . . 9 3 Feature Extraction Under Pose Variation . . . . . . . . . . . . . . .12 3.1 Multiple Embedded HMMs . . . . . . . . . . . . . . . . . . . . . . . 12 3.2 Facial Feature Extraction and Subspace Construction . . . . . . . . . 13 3.3 Overview of Training Stage . . . . . . . . . . . . . . . . . . . . . . . 14 3.4 Overview of Testing Stage . . . . . . . . . . . . . . . . . . . . . . . 17 4 Face Recognition – Combination of Boosted Features . . . . . . . . . . . 18 4.1 Feature Combination Methods . . . . . . . . . . . . . . . . . . . . . 18 4.2 Distance Measure with Equal Sum . . . . . . . . . . . . . . . . . . . 20 4.3 Boosted Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 4.4 Face Identification and Face Verification . . . . . . . . . . . . . . . . 21 5 Experiments and Results . . . . . . . . . . . . . . . . . .. 23 5.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 5.1.1 PIE Database . . . . . . . . . . . . . . . . . . . . . . . . . . 24 5.1.2 ORL Database . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.1.3 Our Database . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.2 Experimental Results and Comparison with other methods . . . . . . 26 5.2.1 PIE Database . . . . . . . . . . . . . . . . . . . . . . . . . . 30 5.2.2 ORL Database . . . . . . . . . . . . . . . . . . . . . . . . . 33 5.2.3 Our Database . . . . . . . . . . . . . . . . . . . . . . . . . . 36 5.2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 6 Conclusion and Future Work . . . . . . . . . . . . . . . . . .. . .43 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Bibliography . . . . . . . . . . . . . . . . . .. . . . . . . 45 | |
dc.language.iso | en | |
dc.title | 利用嵌入隱藏式馬可夫模型擷取人臉特徵的人臉辨識系統 | zh_TW |
dc.title | Facial Feature Extraction Using Embedded Hidden Markov Model for Face Recognition | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 碩士 | |
dc.contributor.coadvisor | 徐繼聖(Jison Hsu) | |
dc.contributor.oralexamcommittee | 王傑智(Chieh-Chih Wang),李明穗(Ming-Sui Lee),莊永裕(Yung-Yu Chuang) | |
dc.subject.keyword | 人臉身份比對,人臉身份確認,基於特徵的人臉辨識,人臉特徵合併, | zh_TW |
dc.subject.keyword | Face Identification,Face Verification,Component-based Face Recognition,Facial Feature Combination, | en |
dc.relation.page | 48 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2007-06-26 | |
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 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-96-1.pdf 目前未授權公開取用 | 1.64 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。