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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪一平 | |
| dc.contributor.author | Ping-Han Lee | en |
| dc.contributor.author | 李秉翰 | zh_TW |
| dc.date.accessioned | 2021-06-15T05:19:16Z | - |
| dc.date.available | 2011-08-02 | |
| dc.date.copyright | 2010-08-02 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-07-20 | |
| dc.identifier.citation | [1] Bouchra Abboud and Mo Dang. Bilinear factorisation for facial expression analysis and synthesis. IEE Proceedings on Vision, Image and Signal Processing, 152(3):327 – 333, 3 2005.
[2] Chiraz Ben Abdelkader and Paul Griffin. A local region-based approach to gender classification from face images. In Workshop of IEEE Conference on Computer Vision and Pattern Recognition, pages 52–52, June 2005. [3] Jrgen Ahlberg. Facial feature extraction using deformable graphs and statistical pattern matching. In in Swedish Symposium on Image Analysis, SSAB, 1999. [4] Timo Ahonen, Abdenour Hadid, and Matti Pietikainen. Face recognition with local binary patterns. In European Conference on Computer Vision, pages 469–481, 2004. [5] Timo Ahonen, Abdenour Hadid, and Matti Pietikainen. Face description with local binary patterns: Application to face recognition. In IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 2037–2041, 2006. [6] Timo Ahonen, Matti Pietikainen, Abdenour Hadid, and Topi Maenpaa. Face recognition based on the appearance of local regions. In ICPR ’04: Proceedings of the Pattern Recognition, 17th International Conference on (ICPR’04) Volume 3, pages 153–156, Washington, DC, USA, 2004. IEEE Computer Society. [7] Ergun Akleman. Making caricatures with morphing. In SIGGRAPH ’97: ACM SIGGRAPH 97 Visual Proceedings: The art and interdisciplinary programs of SIGGRAPH ’97, page 145, New York, NY, USA, 1997. ACM. [8] Ergun Akleman and Jon Reisch. Modeling expressive 3d caricatures. In SIGGRAPH, page 61, New York, NY, USA, 2004. ACM. 138 [9] Keith Anderson and Peter W McOwan. A real-time automated system for the recognition of human facial expressions. 36(1):96 –105, feb. 2006. [10] Stefano Arca, Paola Campadelli, and Raffaella Lanzarotti. A face recognition system based on local feature analysis. In Audio and Video-Based Biometric Person Authentication, volume 2688, page 1060, 2003. [11] Stefano Arca, Paola Campadelli, and Raffaella Lanzarotti. A face recognition system based on automatically determined facial fiducial points. Pattern Recognition, 39:432–443, 2006. [12] Shumeet Baluja and Henry A. Rowley. Boosting sex identification performance. International Journal of Computer Vision, 71(1):111–119, 2007. [13] Evgeniy Bart, Evgeny Byvatov, and Shimon Ullman. View-invariant recognition using corresponding object fragments. In European Conference on Computer Vision, pages Vol II: 152–165, 2004. [14] Marian Stewart Bartlett, H. Martin Lades, and Terrence Sejnowski. Independent component representation for face recognition. In Proceedings of the SPIE Symposium on Electronic Imaging: Science and Technology, pages 528–539, 1998. [15] Marian Stewart Bartlett and Terrence J. Sejnowski. Viewpoint invariant face recognition using independent component analysis and attractor networks. In Advances in Neural Information Processing Systems 9, pages 817–823, 1997. [16] Peter N. Belhumeur, Joao P. Hespanha, and David J. Kriegman. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(7):711–720, 1997. [17] Volker Blanz and Thomas Vetter. A morphable model for the synthesis of 3d faces. In SIGGRAPH, pages 187–194, 1999. [18] F. Bourel, C. C. Chibelushi, and A. A. Low. Robust facial expression recognition using a state-based model of spatially-localised facial dynamics. In FGR ’02: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, page 113, Washington, DC, USA, 2002. IEEE Computer Society. [19] Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm. [20] Hong Chen, Ying qing Xu, Heung yeung Shum, Song chun Zhu, and Nan ning Zheng. Example-based facial sketch generation with non-parametric sampling. In IEEE International Conference on Computer Vision, pages 433–438, 2001. [21] Yu-Lun Chen, Wen-Hung Liao, and Pei-Ying Chiang. Generation of 3d caricature by fusing caricature images. SMC, 1:866–871, Oct. 2006. [22] Hui cheng Lian and Bao liang Lu. Multi-view gender classification using multi-resolution local binary patterns and support vector machines. International Journal of Neural Systems, 17(6):479–487, 2007. [23] Tim Cootes, Camillo J. Taylor, D. Cooper, and J. Graham. Active shape models - Their training and application. Computer Vision and Image Understanding, 61:38–59, 1995. [24] Eric Cosatto and Hans Peter Graf. Photo-realistic talking-heads from image samples. IEEE Transactions on Multimedia, 2(3):152 –163, sep 2000. [25] Thomas G. Dietterich and Ghulum Bakiri. Error-correcting output codes: a general method for improving multiclass inductive learning programs. In Proceedings of the Ninth AAAI National Conference on Artificial Intelligence, pages 572–577, 1991. [26] Thomas G. Dietterich and Ghulum Bakiri. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2:263–286, 1995. [27] Gianluca Donato, Marian Stewart Bartlett, Joseph C. Hager, Paul Ekman, and Terrence J. Sejnowski. Classifying facial actions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(10):974–989, 2000. [28] Peng Du and Ding Xiaoqing. The application of decision tree in gender classification. In Image and Signal Processing, 2008. CISP ’08. Congress on, volume 4, pages 657–660, May 2008. [29] Stefan Eickeler, Stefan Muller, and Gerhard Rigoll. High performance face recognition using pseudo 2-d hidden markov models. European Control Conference, 1999. [30] Paul Ekman and Wallace V. Friesen. Facial action coding system: A technique for the measurement of facial movement. In Consulting Psychologists Press, 1978. 140 [31] Meng Joo Er, Weilong Chen, and Shiqian Wu. High-speed face recognition based on discrete cosine transform and rbf neural networks. IEEE Transactions on Neural Networks, 16(3), May 2005. [32] Roman Erenshteyn, David Saxeo, Pavel Laskovo, and Richard Foulds. Distributed output encoding for multi-class pattern recognition. In International Conference on Image Analysis and Processing 1999, pages 229–234, 1999. [33] Irfan A. Essa. Coding, analysis, interpretation, and recognition of facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19:757–763, 1998. [34] Yuchun Fang and Zhan Wang. Improving lbp features for gender classification. In Wavelet Analysis and Pattern Recognition, 2008. ICWAPR ’08. International Conference on, volume 1, pages 373–377, Aug. 2008. [35] Mario A.F. Figueiredo and Anil K. Jain. unsupervised learning of finite mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24:381–396, 2002. [36] Guanhua Fu, Yiqiang Chen, Junfa Liu, Jingye Zhou, and Pengfei Li. Interactive expressive 3d caricatures design. IEEE International Conference on Multimedia and Expo, pages 965–968, 23 2008-April 26 2008. [37] T. Fujiwara, H. Koshimizu, K. Fujimura, G. Fujita, Y. Noguchi, and N. Ishikawa. A method for 3d face modeling and caricatured figure generation. IEEE International Conference on Multimedia and Expo, 2:137–140 vol.2, 2002. [38] Bruce Gooch, Erik Reinhard, and Amy Gooch. Human facial illustrations: Creation and psychophysical evaluation. ACM Trans. Graph, 23:27–44, 2004. [39] Venkatesan Guruswami and Amit Sahai. Multiclass learning, boosting, and error-correcting codes. In In Proceedings of the Twelfth Annual Conference on Computational Learning Theory, pages 145–155, 1999. [40] Venkatesan Guruswami and Amit Sahai. Multiclass learning, boosting, and error-correcting codes. pages 145 – 155, 1999. [41] Srinivas Gutta and Harry Wechsler. Gender classification of human faces using hybrid classifier systems. In Neural Networks,1997., International Conference on, volume 3, pages 1353–1358 vol.3, Jun 1997. [42] Z. Hafed and M. Levine. Face recognition using discrete cosine transform. International Journal of Computer Vision, 43(3):167–188, 2001. 141 [43] Xia Han, Hassan Ugail, and Ian Palmer. Gender classification based on 3d face geometry features using svm. In CyberWorlds, 2009. CW ’09. International Conference on, pages 114–118, Sept. 2009. [44] Xiaofei He, Shuicheng Yan, Yuxiao Hu, Partha Niyogi, and Hong-Jiang Zhang. Face recognition using laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell., 27(3):328–340, 2005. [45] Bernd Heisele, Purdy Ho, Jane Wu, and Tomaso Poggio. Face recognition: component-based versus global approaches. Computer Vision and Image Understanding, 91(1):6–12, 2003. [46] Bernd Heisele, Thomas Serre, and Tomaso Poggio. A component-based framework for face detection and identification. International Journal of Computer Vision, 74(2):167–181, 2007. [47] Bernd Heisele, Serre Thomas, Prentice Sam, and Tomaso Poggio. Hierarchical classification and feature reduction for fast face detection with support vector machines. Pattern Recognition, 36(9):2007–2017, 2003. [48] Rein-Lien Hsu and A.K. Jain. Generating discriminating cartoon faces using interacting snakes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(11):1388–1398, Nov. 2003. [49] Jian Huang, Pong C. Yuen, Wen-Sheng Chen, and J. H. Lai. Componentbased lda method for face recognition with one training sample. In AMFG ’03: Proceedings of the IEEE International Workshop on Analysis and Modeling of Faces and Gestures, page 120, Washington, DC, USA, 2003. IEEE Computer Society. [50] Yea-Shuan Huang and Wei-Cheng Liu. Face recognition based on complementary matching of single image and sequential images. International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 0:673–676, 2009. [51] Yuri Ivanov, Bernd Heisele, and Thomas Serre. Using component features for face recognition. In Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, page 421, 2004. [52] Simon J. D. Prince Jania Aghajanian. Mosaicfaces: a discrete representation for face recognition. In Proceedings of the IEEE Workshop on Applications of Computer Vision, pages 1–8, 2008. 142 [53] Michael J. Jones and Paul Viola. Face recognition using boosted local features. Technical report, 2003. [54] Masahide Kaneko and Mitsuhiko Meguro. Synthesis of facial caricatures using eigenspaces and its applications to humanlike animated agents. In International Workshop on Lifelike Animated Agents Tools, Affective Functions, and Applications, pages 58–63, 2002. [55] Henry Kang and Seungyong Lee. Shape-simplifying image abstraction. In Computer Graphics Forum: Special issue on the Pacific Graphics, number 7, pages 1773–1780, 2008. [56] Scott A. King and Richard E. Parent. Creating speech-synchronized animation. IEEE Transactions on Visualization and Computer Graphics, 11(3):341 –352, may-june 2005. [57] J. Kittler, R. Ghaderi, T. Windeatt, and J. Matas. Face verification using error correcting output codes. In IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages I–755– I–760, 01. [58] Vinayadatt V. Kohir and U. B. Desai. Face recognition using a dct-hmm approach. In IEEE Workshop on Applications of Computer Vision, pages 226–231, 1998. [59] T. Kondo, K. Murakami, and H. Koshimizu. From coarse to fine correspondence of 3-d facial images and its application to 3-d facial caricaturing. Proceeding of the International Conference on 3-D Digital Imaging and Modeling, pages 283–288, May 1997. [60] Irene Kotsia and Ioannis Pitasy. Facial expression recognition in image sequences using geometric deformation features and support vector machines. IEEE Transactions on Image Processing, 16(1):172 –187, jan. 2007. [61] Ka H. Lai, Eran A. Edirisinghe, and Paul W. H. Chung. A facial component based hybrid approach to caricature generation using neural networks. In Computational Intelligence, pages 174–179, 2006. [62] Ping-Han Lee, Lu-Jong Chu, Yi-Ping Hung, Sheng-Wen Shih, Chu-Song Chen, and Hsin-Min Wang. Cascading multimodal verification using face, voice and iris information. In IEEE International Conference on Multimedia and Expo, pages 847–850, Beijing, China, July 2007. [63] Ping-Han Lee, Gee-Sern Hsu, and Yi-Ping Hung. Face verification and identification using facial trait code. IEEE Conference on Computer Vision and Pattern Recognition, pages 1613–1620, 2009. 143 [64] Ping-Han Lee, Gee-Sern Hsu, and Yi-Ping Hung. Polymorphous facial trait code. In Asian Conference on Computer Vision, pages 560–569, 2009. [65] Ping-Han Lee and Yi-Ping Hung. Face synthesis using facial trait code and its application to creating suspect’s physical profiles. In IEEE International Conference on Multimedia and Expo, pages 286–289, 2009. [66] Ping-Han Lee, Yun-Wen Wang, Jison Hsu, Ming-Hsuan Yang, and Yi-Ping Hung. Robust facial feature extraction using embedded hidden markov model for face recognition under large pose variation. Institute of Industrial Science, University of Tokyo, JAPAN, 2007. [67] Ping-Han Lee, Yun-Wen Wang, Ming-Hsuan Yang, Jison Hsu, and Yi-Ping Hung. Distinctive personal traits for face recognition under occlusion. In 2006 IEEE International Conference on Systems, Man, and Cybernetics, pages 4202–4207, The Grand Hotel, Taipei, Taiwan, 2006. [68] XueMing Leng and YiDing Wang. Improving generalization for gender classification. In IEEE International Conference on Image Processing, pages 1656–1659, Oct. 2008. [69] Yuanzhong Li and H. Kobatake. Extraction of facial sketch image based on morphological processing. IEEE International Conference on Image Processing, 3:316–319 vol.3, Oct 1997. [70] Lin Liang, Hong Chen, Ying-Qing Xu, and Heung-Yeung Shum. Examplebased caricature generation with exaggeration. Proceedings. 10th Pacific Conference on Computer Graphics and Applications, pages 386–393, 2002. [71] Rui Liao and Stan Z. Li. Face recognition based on multiple facial features. In In Proc. of the 4th IEEE Int. Conf. on Automatic Face and Gesture Recognition, pages 239–244. Dekker Inc, 2000. [72] Jenn-Jier James Lien, Takeo Kanade, Jeffrey Cohn, and Ching-Chung Li. Automated facial expression recognition based on facs action units. In IEEE International Conference on Automatic Face and Gesture Recognition, pages 390–395, 1998. [73] Shu Lin and Daniel J. Costello. Error Control Coding: Fundamentals and Applications. Prentice-Hall, 1983. [74] Chengjun Liu. Gabor-based kernel pca with fractional power polynomial models for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5):572–581, May 2004. 144 [75] Chengjun Liu and Harry Wechsler. Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Transactions on Image Processing, 11(4):467–476, April 2002. [76] Chengjun Liu and Harry Wechsler. Independent component analysis of gabor features for face recognition. IEEE Transactions on Neural Networks, 14(4):919–928, July 2003. [77] Li Lu and Pengfei Shi. A novel fusion-based method for expression-invariant gender classification. In IEEE International Conference on Acoustics, Speech and Signal Processing, pages 1065–1068, April 2009. [78] Li Lu, Ziyi Xu, and Pengfei Shi. Gender classification of facial images based on multiple facial regions. In 2009 WRI World Congress on Computer Science and Information Engineering, volume 6, pages 48–52, 31 2009-April 2 2009. [79] Wei Lu, Yi Xu, Xiaokang Yang, and Li Song. Local quaternionic gabor binary patterns for color face recognition. In IEEE International Conference on Acoustics, Speech and Signal Processing, 2008. [80] Thomas Luft and Oliver Deussen. Non-photorealistic real-time rendering of characteristic faces. Computer Graphics and Applications, 2004. PG 2004. Proceedings. 12th Pacific Conference on, pages 339–347, Oct. 2004. [81] L. Ma and K. Khorasani. Facial expression recognition using constructive feedforward neural networks. 34(3):1588 –1595, june 2004. [82] Aleix Mart Martinez and Robert Benavente. The ar face database. Technical Report 24, CVC, 1998. [83] K Messer, J Matas, J Kittler, J Luettin, and G Maitre. Xm2vtsdb: The extended m2vts database. In Proceedings of the Second International Conference on Audio and Video-based Biometric Person Authentication, 1999. [84] Stephen Milborrow and Fred Nicolls. Locating facial features with an extended active shape model. In European Conference on Computer Vision, pages 504–513, Berlin, Heidelberg, 2008. Springer-Verlag. [85] Baback Moghaddam and Ming-Hsuan Yang. Learning gender with support faces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):707–711, 2002. 145 [86] Ara V. Nefian and Monson Hayes. An embedded HMM-based approach for face detection and recognition. In IEEE International Conference on Acoustics, Speech and Signal Processing, volume 6, pages 3553–3556, 1999. [87] Ara V. Nefian and Monson H. Hayes III. Maximum likelihood training of the embedded hmm for face detection and recognition. In IEEE International Conference on Image Processing, pages Vol I: 33–36, 2000. [88] H. Othman and T. Aboulnasr. A separable low complexity 2d hmm with application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10):1229–1238, 2003. [89] Zhengjun Pan, Rod Adams, and Hamid Bolouri. Dimensionality reduction of face images using discrete cosine transforms for recognition. In IEEE Conference on Computer Vision and Pattern Recognition, 2000. [90] M. Pantic and L. J. M. Rothkrantz. An expert system for multiple emotional classification of facial expressions. In ICTAI ’99: Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence, page 113, Washington, DC, USA, 1999. IEEE Computer Society. [91] M. Pantic and L.J.M. Rothkrantz. Facial action recognition for facial expression analysis from static face images. 34(3):1449 –1461, june 2004. [92] Alex Pentland, Baback Moghaddam, and Thad Starner. View-based and modular eigenspaces for face recognition. In IEEE Conference on Computer Vision and Pattern Recognition, pages 84–91, 1994. [93] Jonathon P. Phillips, Todd W. Scruggs, Alice J. O’toole, Patrick J. Flynn, Kevin W. Bowyer, Cathy L. Schott, and Matthew Sharpe. Frvt 2006 and ice 2006 large-scale results. Technical report, National Institute of Standards and Technology, March 2007. [94] P. Jonathon Phillips, P.J. Flynn, T. Scruggs, K.W. Bowyer, Jin Chang, K. Hoffman, J. Marques, Jaesik Min, and W. Worek. Overview of the face recognition grand challenge. In IEEE Conference on Computer Vision and Pattern Recognition, pages 947– 954, 2005. [95] P. Jonathon Phillips, Hyeonjoon Moon, Syed A. Rizvi, and Patrick J. Rauss. The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(10):1090– 1034, 2000. 146 [96] J. Ross Quinlan. C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993. [97] M.E. Sargin, Y. Yemez, E. Erzin, and A.M. Tekalp. Analysis of head gesture and prosody patterns for prosody-driven head-gesture animation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 30(8):1330 – 1345, aug. 2008. [98] M. Savvides, R. Abiantun, J. Heo, S. Park, C. Xie, and B.V.K. Vijayakumar. Partial and holistic face recognition on frgc-ii data using support vector machine. In Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW06), pages 48–48, 2006. [99] Fabien Scalzo, George Bebis, Mircea Nicolescu, and Leandro Loss. Feature fusion hierarchies for gender classification. In IAPR International Conference on Pattern Recognition, pages 1–4, Dec. 2008. [100] Robert E. Schapire. Using output codes to boost multiclass learning problems. In ICML ’97: Proceedings of the Fourteenth International Conference on Machine Learning, pages 313–321, San Francisco, CA, USA, 1997. Morgan Kaufmann Publishers Inc. [101] Nasim Shams, Iraj Hosseini, Mohammad Sadegh Sadri, and Ehsan Azarnasab. Low cost fpga-based highly accurate face recognition system using combined wavelets with subspace methods. In IEEE International Conference on Image Processing, 2006. [102] Caifeng Shan, Shaogang Gong, and Peter W. McOwan. Facial expression recognition based on local binary patterns: A comprehensive study. Image and Visual Computing, 27(6):803–816, 2009. [103] Shiguang Shan, Wen Gao, Jie Yan, Hongming Zhang, and Xilin Chen. Individual 3d face synthesis based on orthogonal photos and speech-driven facial animation. IEEE International Conference on Image Processing, 3:238–241 vol.3, 2000. [104] Wen sheng Vincnent Chu, Ju chin Chen, and Jenn jier James Lien. Kernel discriminant analysis based on canonical differences for face recognition in image sets. In Asian Conference on Computer Vision, 2007. [105] Rupesh N. Shet, Ka H. Lai, Eran A. Edirisinghe, and Paul W.H. Chung. Use of neural networks in automatic caricature generation: An approach based on drawing style capture. In The IEE International conference on Visual Information Engineering, pages 23–29, 2005. 147 [106] Terence Sim, Simon Baker, and Maan Bsat. The CMU pose, illumination, and expression (PIE) database of human faces. Technical Report CMU-RITR- 01-02, Carnegie Mellon University, 2001. [107] Mingli Song, Zhao Dong, C. Theobalt, Huiqiong Wang, Zicheng Liu, and H.-P. Seidel. A generic framework for efficient 2-d and 3-d facial expression analogy. IEEE Transactions on Multimedia, 9(7):1384 –1395, nov. 2007. [108] Mingli Song, Dacheng Tao, Zicheng Liu, Xuelong Li, and Mengchu Zhou. Image ratio features for facial expression recognition application. 40(3):779 –788, june 2010. [109] Ning Sun, Wenming Zheng, Changyin Sun, Cairong Zou, and Li Zhao. Gender classification based on boosting local binary pattern. In International Symposium on Neural Networks(ISNN), volume 2, pages 194–201, 2006. [110] Zehang Sun, G. Bebis, Xiaojing Yuan, and S.J. Louis. Genetic feature subset selection for gender classification: a comparison study. In Applications of Computer Vision, 2002. (WACV 2002). Proceedings. Sixth IEEE Workshop on, pages 165–170, 2002. [111] Ying-Li Tian, Takeo Kanade, and Jeffrey Cohn. Recognizing action units for facial expression analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(1):97 – 115, February 2001. [112] Fok Hing Chi Tivive and A. Bouzerdoum. A shunting inhibitory convolutional neural network for gender classification. In IAPR International Conference on Pattern Recognition, volume 4, pages 421–424, 0-0 2006. [113] N. Tokuda, T. Hoshino, T. Watanabe, T. Funahashi, T. Fujiwara, and H. Koshimizu. Caricature generation system picasso-2 exhibited at expo2005 and its performance improvement. International Conference on Control, Automation and Systems, pages 1354–1358, Oct. 2007. [114] Chien Chung Tseng and Jenjier James Lien. Synthesis of exaggerative caricature with inter and intra correlations. In Asian Conference on Computer Vision, pages 314–323, 2007. [115] Matthew Turk and Alex Pentland. Eigenfaces for recognition. Journal of Cognitive Neuroscience, 3(1):71–86, 1991. [116] Kazuya Ueki, Hiromitsu Komatsu, Satoshi Imaizumi, Kenichi Kaneko, Nobuhiro Sekine, Jiro Katto, and Tetsunori Kobayashi. A method of gender 148 classification by integrating facial, hairstyle, and clothing images. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, volume 4, pages 446–449 Vol.4, Aug. 2004. [117] Paul Viola and Michael Jones. Rapid object detection using a boosted cascade of simple features. In IEEE Conference on Computer Vision and Pattern Recognition, volume 1, pages 511–518, 2001. [118] Paul Viola and Michael J. Jones. Robust real-time face detection. Int. J. Comput. Vision, 57(2):137–154, 2004. [119] Paul Viola, Gregory Shakhnarovich, Gregory Shakhnarovich, Paul A. Viola, Baback Moghaddam, and Baback Moghaddam. A unified learning framework for real time face detection and classification. In IEEE International Conference on Automatic Face and Gesture Recognition, 2002. [120] Te-Hsun Wang and Jenn-Jier James Lien. Facial expression recognition system based on rigid and non-rigid motion separation and 3d pose estimation. Pattern Recognition, 42(5):962–977, 2009. [121] Xiaogang Wang and Xiaoou Tang. Face photo-sketch synthesis and recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(11):1955 –1967, nov. 2009. [122] Xiaogang Wang and Xiaoou Tong. Improving indoor and outdoor face recognition using unified subspace analysis and gabor features. In IEEE International Conference on Image Processing, 2004. [123] T. Windeatt and G. Ardeshir. Boosted ecoc ensembles for face recognition. In The IEE International conference on Visual Information Engineering, pages 165 –168, 2003. [124] John Wright, Allen Y. Yang, Arvind Ganesh, Shankar S. Sastry, and Yi Ma. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(2):210–227, 2009. [125] Jing Wu, W.A.P. Smith, and E.R. Hancock. Gender classification using shape from shading. In British Machine Vision Conference, pages xx–yy, 2007. [126] Jing Wu, W.A.P. Smith, and E.R. Hancock. Gender classification based on facial surface normals. In Pattern Recognition, 2008. ICPR 2008. 19th International Conference on, pages 1–4, Dec. 2008. 149 [127] Bin Xia, He Sun, and Bao-Liang Lu. Multi-view gender classification based on local gabor binary mapping pattern and support vector machines. In Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on, pages 3388– 3395, June 2008. [128] Chunyan Xie and B.V.K. Vijaya Kumar. Face class code based feature extraction for face recognition. In Fourth IEEE Workshop on Publication Automatic Identification Advanced Technologies, pages 257–262, 2005. [129] Xudong Xie and Kin-Man Lam. Gabor-based kernel pca with doubly nonlinear mapping for face recognition with a single face image. IEEE International Conference on Multimedia and Expo, 15(9):2481–2492, September 2006. [130] Guang Zhe Xu, Masahide Kaneko, and Akira Kurematsu. Synthesis of facial caricature using eigenspaces. In Electronics and Communications in Japan (Part III: Fundamental Electronic Science), pages 43–54, 2004. [131] Zijian Xu, Hong Chen, and Song-Chun Zhu. A high resolution grammatical model for face representation and sketching. In IEEE Conference on Computer Vision and Pattern Recognition, volume 2, pages 470–477, 2005. [132] Chuan xu Wang, Yun Liu, and Zuo yong Li. Algorithm research of face image gender classification based on 2-d gabor wavelet transform and svm. In Computer Science and Computational Technology, 2008. ISCSCT ’08. International Symposium on, volume 1, pages 312–315, Dec. 2008. [133] Ming-Hsuan Yang, David J. Kriegman, and Narendra Ahuja. Detecting faces in images: A survey. IEEE Trans. Pattern Anal. Mach. Intell., 24(1):34–58, 2002. [134] S.N. Yang, K.S. Huang, Y.C. Tseng, and C.Y. Wang. Avatar facial animation synthesizer for mobile clients. The Second International Conference on Advances in Mobile Multimedia, 22-24 September 2004. [135] Bangpeng Yao, Haizhou Ai, and Shihong Lao. Person-specific face recognition in unconstrained environments: a combination of offline and online learning. In 8th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2008), Amsterdam, The Netherlands, September 17-19 2008. [136] Bai-Ling Zhang, Haihong Zhang, and Shuzhi Sam Ge. Face recognition by applying wavelet subband representation and kernel associative memory. IEEE Transactions on Neural Networks, 15(1):166–177, January 2004. 150 [137] Baochang Zhang, Shiguang Shan, Xilin Chen, and Wen Gao. Histogram of gabor phase patterns (hgpp): A novel object representation approach for face recognition. In IEEE Transactions on Image Processing, pages 57–68, 2007. [138] Lei Zhang, Sen Wang, and Dimitris Samaras. Face synthesis and recognition from a single image under arbitrary unknown lighting using a spherical harmonic basis morphable model. In IEEE Conference on Computer Vision and Pattern Recognition, pages 209–216, 2005. [139] Qingshan Zhang, Z. Liu, Gaining Quo, D. Terzopoulos, and Heung-Yeung Shum. Geometry-driven photorealistic facial expression synthesis. IEEE Transactions on Visualization and Computer Graphics, 12(1):48 –60, jan.- feb. 2006. [140] Wenchao Zhang, Shiguang Shan, Laiyun Qing, Xilin Chen, and Wen Gao. Are gabor phases really useless for face recognition? In Pattern Analysis & Applications, London, 5 2008. Springer. [141] Guoying Zhao and M. Pietikainen. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6):915 –928, june 2007. [142] Jiali Zhao, Haitao Wang, Haibing Ren, and Seok-Cheol Kee. Lbp discriminant analysis for face verification. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) - Workshops, page 167. IEEE Computer Society, 2005. [143] Shuyan Zhao and Rolf-Rainer Grigat. Multiblock-fusion scheme for face recognition. In ICPR ’04: Proceedings of the Pattern Recognition, 17th International Conference on (ICPR’04) Volume 1, pages 309–312,Washington, DC, USA, 2004. IEEE Computer Society. [144] Jianke Zhu, Mang I Vai, and Peng Un Mak. Face recognition using 2d dct with pca. In The 4nd Chinese Conference on Biometric Recognition (Sinobiometrics’03), 2003. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46621 | - |
| dc.description.abstract | 我們提出了人臉特徵碼來對人臉進行編碼與分析。藉由大量的人臉資料，我們定義了重要人臉局部特徵以及其主要類型，這些特徵與類型能有效描述人臉。人臉特徵與類型的建立有兩個步驟：第一個步驟使用沒有光線與表情變化的人臉來進行特徵類型的定義與重要人臉特徵的選取，這些重要特徵與類型能有效區隔不同人之人臉; 第二個步驟考慮了有光線與表情變化的人臉來建立人臉編碼器，以增加辨識人臉辨識之強健性。人臉特徵碼的編碼結果有兩種型態：整數碼與機率碼，前者所需記憶體較小，後者提供較高之辨識準確度。人臉特徵碼可以應用於人臉身份辨識、性別辨識、表情辨識，以及人臉合成上。
在身份辨識的應用上，人臉首先被編碼，然後再與資料庫中已知人臉特徵碼進行比對而得知其身份。在我們的實驗中，人臉特徵碼與目前幾個主流的人臉辨識方法相比有較高的辨識準確度。我們也將人臉特徵碼的方法實作並整合至一自動化的人臉辨識系統。 至於表情辨識方面的應用，我們進一步的定義了表情類型。表情類型捕捉了同一個人之人臉因表情不同而產生的變化。有了表情類型，我們根據表情類型建立各種不同表情的機率模型，並據此進行人臉表情辨識。 針對身份辨識應用所選出之主要類型能有效區分不同人，但這些類型不見的能有效區分性別。我們也選出了能夠有效區分性別之性別特徵，並使用性別特徵來進行性別辨識。 人臉特徵碼的解碼程序可視應用而有所不同。除了上述應用於人臉辨識的解碼外，我們也提出了應用於人臉合成的解碼程序，並且展示了人臉特徵碼可用來合成栩栩如生的人臉。 | zh_TW |
| dc.description.abstract | We propose Facial Trait Code (FTC) to encode human facial images for facial analysis. Extracted from an exhaustive set of local patches cropped from a large stack of faces, the facial traits and the associated trait patterns can accurately capture the appearance of a given face. The extraction has two phases. The first phase is composed of clustering and boosting upon a training set of faces with neural expression, even illumination, and frontal pose. The second phase focuses on the extraction of the facial trait patterns from the faces with variations in expression, illumination, and poses. With two different metrics for characterizing the facial trait patterns, the FTC can take either hard or probabilistic codewords. The former offers a concise representation to a face; the latter enables codeword matching with superb accuracy. The proposed FTC can be applied to face identification and verification, expression recognition, gender recognition, and face synthesis. For the application to face identification and verification, a face is encoded as a codeword, and this codeword is matched against known codewords. Experiments reveal that the FTC outperforms many face recognition algorithms, including a couple considered as the most potential ones in recent years. For the application of FTC to expression recognition, the external patterns that encode the variation among facial images caused by different facial expressions are identified, and the pattern-to-expression probabilities are learned. The facial traits are best for discrimination of different individuals. Besides facial traits, we also select the gender traits that are effective in discriminating different genders and use them for gender recognition. The FTC decoding can be task-oriented. We propose the decoding scheme for face synthesis and demonstrate it can synthesize random, life-like faces effectively. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T05:19:16Z (GMT). No. of bitstreams: 1 ntu-99-D93922027-1.pdf: 18313303 bytes, checksum: f2209af69673f0cb9ce1ac962c72e725 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii 英文摘要 iv List of Tables 5 List of Figures 8 1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 1.1 Applications of FTC and the Organization of this Dissertation . . 21 2 Face Recognition and Analysis: A Historical Review . . . . . . . . 23 2.1 A Review on Feature Extraction Schemes for Facial Analysis . . . . 23 2.1.1 Statistical Features . . . . . . . . . . . . . . . . . . . .. . 23 2.1.2 Manifold Learning . . . . . . . . . . . . . . . . . . . .. . . 24 2.1.3 Features from Frequency Domain . . . . . . . . . . . .. . . . . 26 2.1.4 Local Binary Pattern . . . . . . . . . . . . . . . . .. . . . . 28 2.2 Face Recognition using Local Facial Features . . . . . . . . . . . 29 2.2.1 Implicitly Perceivable Features . . . . . . . . . . . . . . . . 30 2.2.2 Local Facial Blocks . . . . . . . . . . . . .. . . . . . . . . 31 2.3 Facial Coding for Face Recognition . . . . . . .. . . . . . . . . 32 2.4 A Review on Expression Recognition . . . . . . . . . . . . . . . . 36 2.4.1 Facial Action Coding . . . . . . . . . . . . .. . . . . . . . . . 37 2.4.2 Local Binary Pattern . . . . . . . . . . . . .. . .. . . . . . . 41 2.4.3 Other Features . . . . . . . . . . . . . . . .. . .. . . . . . . 41 2.5 A Review on Gender Recognition . . . . . . . . . .. .. . . . . . . 43 2.5.1 Local Binary Pattern . . . . . . . . . . . . .. . .. . . . . . . 44 2.5.2 Wavelet Analysis . . . . . . . . . . . . . . . .. . . . . . . . 45 2.5.3 Part-Based Approaches . . . . . . . . . . . . . . . . . . . . . 46 2.6 A Review on Face Synthesis . . . . . . . . . . . . . . . . . . . . 47 2.6.1 Sketch . . . . . . . . . . . . . . . . . . . . . . . .. . . . . 47 2.6.2 Image Abstraction and Cartoon . . . . . . . . . . . . . . . . . 48 2.6.3 Face caricaturing . . . . . . . . . . . . . . . . . . . . . . . 49 2.6.4 Expression Synthesis . . . . . . . . . . . . . . . . .. . . . . 50 2.6.5 Talking Head Synthesis . . . . . . . . . . . . . . . .. . . . . 50 3 Facial Trait Code. . . . . . . . . . . . . . . . . . . . .. . . . . 52 3.1 Extraction of Facial Traits and Their Patterns . . . . .. . . . . 55 3.1.1 Patches and Patch Patterns . . . . . . . . . . . . . . . . . . . 55 3.1.2 From Patches to Facial Traits . . . . . . . . . . . . . . . . . . 57 3.2 FTC Encoding and Decoding for Face Recognition . . . .. . . . . . 65 3.2.1 Encoding with SVM Classifiers Built on Extended Trait Samples . 65 3.2.2 Hard Codewords to Probabilistic Codewords . . . . . . . . . . . 66 3.2.3 Decoding for Face Recognition . . . . . . . . . . .. . . . . . 67 3.2.4 Hard Codewords versus Probabilistic Codewords . . .. . . . . . 69 4 Face Recognition Using FTC. . . . . . . . . . . . . . . . . . . . . 72 4.1 Study on the Code Length Issue . . . . . . . . . . . . .. . . . . 76 4.2 Performance Comparison with Other Algorithms . . . . . .. . . . . 81 4.3 Integrated with a Face Recognition System . . . . . . .. . . . . . 87 4.3.1 Face and Facial Feature Detection . . . . . . . . . .. . . . . 88 4.3.2 Face Normalization . . . . . . . . . . . . . . . . . . . . . . . 89 4.3.3 Non-Face Rejection . . . . . . . . . . . . . . . . . . . . . . . 90 4.3.4 Face Recognition using FTC . . . . . . . . . . . . . . . . . . . 92 5 Expression Recognition using Polymorphous FTC. . . . . . . . . . . . 94 5.1 Extraction of Polymorphous Patterns . . . . . .. . . . . . . . . . 95 5.1.1 The First Stage of Clustering: Extraction of Inherent Patterns . 96 5.1.2 The Second Stage of Clustering: Extraction of External Patterns . 96 5.2 Polymorphous Patterns for Illumination and Expression Robust Encoding 98 5.3 Expression Recognition Using Polymorphous FTC . . . . . . . . . . . 100 5.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . 102 6 Gender Recognition Using FTC. . . . . . . . . . . . . . . . . . . . . 110 6.1 Selection of Gender-Discriminating Traits . . . . . . . . . . . . . 110 6.2 Gender Recognition Using Gender Traits . . . . . . . . . . . . . . 113 6.3 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . 114 7 Face Synthesis Using FTC. . . . . . . . . . . . . . . . . . .. . . . 121 7.1 FTC Decoding for Face Synthesis . . . . . . . . . . . . . . . . . . 121 7.2 Construction of the FTCAR . . . . . . . . . . . . . . . . . . . . . 122 7.3 Codeword Legitimacy . . . . . . . . . . . . . . . . . . . . . . . . 127 7.4 Random Face Synthesis Using FTC . . . . . . . . . . . . . . . . . . 128 7.5 Face Synthesis Across Genders . . . .. . . . . . . . . . . . . . . 132 8 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 Bibliography. . . . . . . . . . . . . . . . . . . . . . . . .138 Index. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 Glossary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 A Error-Correcting Property of the ECOC. . . . . . . . . . . . . . . . . 156 | |
| dc.language.iso | en | |
| dc.title | 人臉特徵碼其及應用 | zh_TW |
| dc.title | Facial Trait Code and its Applications | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 歐陽明,林智仁,莊永裕,陳祝嵩,李錫堅,黃雅軒,連震杰 | |
| dc.subject.keyword | 人臉辨識,人臉編碼,性別辨識,表情辨識,人臉合成, | zh_TW |
| dc.subject.keyword | face recognition,facial coding,gender recognition,facial expression recognition,face synthesis, | en |
| dc.relation.page | 156 | |
| 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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