Chapter 6 Conclusions and Future Work
6.2 Future Work
Gabor wavelet based feature extraction method has robust properties against the changes of lighting conditions, but the computation cost of extracting facial points is very high. Moreover, extracting feature points around the mouth is not stable enough.
69
In the future, some popular facial feature models such as active appearance model (AAM) can be combined with Gabor based feature extraction method to improve the performance of extracting facial points. One alternative solution is to use Gabor based feature matching to detect fiducial points in the first frame of image sequences. Then, in the subsequent image frames, AAM or other facial feature models are applied to reduce the computation cost and raise the recognition rates of detecting facial points.
On the other hand, we will also work on new methods for improving the error rate of the original trained data with SVM learning in order to apply the proposed learning algorithm to practical robotic applications.
70
References
[1] M. Fujita, “On Activating Human Communications with Pet-type Robot AIBO,”
Proc. of the IEEE, vol. 92, no. 11, pp. 1804-1813, 2004.
[2] P. Ekman and W. V. Frisen, Emotion in the Human Face. Englewood Cliffs, NJ：
Prentice-Hall, 1975.
[3] P. Ekman and W. V. Friesen：The Facial Action Coding System (Consulting Psychologists Press, San Francisco 1978).
[4] http://face-and-emotion.com/dataface/facs/manual/TitlePage.html
[5] M. Pantic and L.J.M. Rothkrantz, “Automatic Analysis of Facial Expressions：
The State of the Art,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 12., pp. 1424 - 1445, 2000.
[6] Gengtao Zhou, Yongzhao Zhan and Jianming Zhang, “Facial Expression Recognition Based on Selective Feature Extraction,” in Proc. of IEEE 6th Int. Conf.
on Intelligent Systems Design and Applications, Shandong, China, 2006, pp. 412 – 417.
[7] F. Bourel, C.C. Chibelushi, and A.A. Low, “Robust Facial Expression Recognition Using a State-Based Model of Spatially-Localised Facial Dynamics,” in Proc. of IEEE 5th Int. Conf. on Automatic Face and Gesture Recognition, Washington, D.C, USA, 2002, pp. 106 – 111.
[8] WeiFeng Liu and ZengFu Wang, “Facial Expression Recognition Based on Fusion of Multiple Gabor Features,” in Proc. of IEEE 18th Int. Conf. on Pattern Recognition, Hong Kong, China, 2006, pp. 536 – 539.
[9] M. Valstar and M. Pantic, “Fully Automatic Facial Action Unit Detection and Temporal Analysis,” in Proc, IEEE Int. Conf. on Computer Vision and Pattern Recognition Workshop, New York, USA, 2006, pp. 149-156.
[10] G. Guo and C.R. Dyer, “Learning from Examples in the Small Sample Case:
Face Expression Recognition,” IEEE Trans. on Systems, Man and Cybernetics, Part B, vol. 35, no. 3, pp. 477 – 488, 2005.
[11] V. Vanik, The Nature of Statistic Learning Theory, New York ： Springer-Verlag,1995.
71
[12] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, NY Cambridge University Press 2000.
[13] M.Yeasin, B. Bullot, and R. Harma, “Recognition of Facial Expressions and Measurement of Levels of Interest From Video,” IEEE Trans. on Multimedia,vol.8, no.3, pp.500-508, 2006.
[14] Zhengyou Zhang, M. Lyons, M. Schuster, and S. Akamatsu, “Comparison Between Geometry-Based and Gabor-Wavelets-Based Facial Expression Recognition Using Multi-Layer Perception,” in Proc. of 3rd IEEE Conf. on Automatic Face and Gesture Recognition, Nara, Japan, 1998, pp. 454 – 459.
[15] Y. Z. Zhan, J. F. Ye, D. J. Niu, and P. Cao, “Facial Expression Recognition Based on Gabor Wavelet Transformation and Elastic Templates Matching,” in Proc. of 3rd Int. Conf. on Image and Graphics, Hong Kong, China, 2004, pp. 254 – 257.
[16] Y. Saatci and C. Town, “Cascaded Classification of Gender and Facial Expression Using Active Appearance Models,” in Proc. of 7th Int. Conf. on Automatic face and Gesture Recognition, Southampton, UK, 2006, pp. 393 – 398.
[17] G. J. Edwards ,T. F. Cootes and C. J. Taylor, “Face Recognition Using Active Appearance Models, ” in Proc. of European Conf. Computer Vision, Freiburg, Germany, 1998, pp.581-695.
[18] I. Kotsia and I. Pitas, “Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines,” IEEE Trans. on Image Processing, vol.16, no.1 , pp. 172-187, 2007
[19] T. Kobayashi, Y. Ogawa, K. Kato and K. Yamamoto,“ Learning Systems of Human Facial Expression for a Family Robot,” in Proc. of the 6th IEEE Int. Conf.
on Automatic Face and Gesture Recognition, Seoul, Korea, 2004, pp. 481-486.
[20] S. Ozawa, S.L. Toh, S. Abe, Pang Shaoning and N. Kasabov,“ Incremental Learning for Online Face Recognition,” In Proc. of Int. Joint Conf. on Neural Networks, Montreal, Canada, 2005, pp. 3174 – 3179.
[21] W. M. Huang, B. H. Lee, L. Y. Li and K. Leman,“ Face Recognition by Incremental Learning,” In Proc. of IEEE Int. Conf. on Systems, Man and Cybernetics, Washington, D.C., USA, 2003, pp.4728-4723.
[22] R. Xiao, J. Wang and F. Zhang, “An Approach to Incremental SVM Learning Algorithm,” in Proc. of the 12th International Conference on Tools with Artificial Intelligence, Vancouver, Canada, 2000, pp.268– 273.
72
[23] J. L. An, Z. O. Wang and Z. P. Ma, “An Incremental Learning Algorithm for Support Vector Machine,” in Proc. of Int. Conf. on Machine Learning and Cybernetics, Xian,, China, 2003, pp. 1153 – 1156.
[24] L. Xuchun, Z. Yan and E. Sung, “Sequential Bootstrapped Support Vector Machines - a SVM accelerator,” in Proc. of IEEE Int. Joint Conf. Neural Networks, Montréal, Canada, 2005, pp. 1437 – 1442.
[25] W. H. Zeng and J. Ma, “A Novel Incremental SVM Learning Algorithm,” in Proc. of Int. Conf. on CSCW in Design, Xiamen, China, 2004, pp. 658 – 662.
[26] P. Mitra, C. A. Murthy and S. K. Pal, “Data Condensation in Large Databases by Incremental Learning With Support Vector Machines,” In Proc. of Intl. Conf. on Pattern Recognition, Catalonia, Spain, 2000, pp. 708 – 711.
[27] Y. Liu, Q. He and Q. Chen, “Incremental Batch Learning with Support Vector Machines,” in Proc. of 5th world congress on Intelligent Control and Automation, Hangzhou, China, 2004, pp. 1857– 1861.
[28] Y. Liu, Q. He and Y. Tang, “Support Vector Pursuit Learning,” in Proc. of IEEE Int. Conf. on System, Man and Cybernetics , The Hague, The Netherlands, 2004, pp.5841– 5846.
[29] C.M. Chou: Real-Time Face Tracking Under Illumination Variation, Master. Eng.
Thesis, National Chiao Tung University, Hsinchu, Taiwan, R.O.C., 2005.
[30] A. McAndrew, Introduction to Digital Image Processing with Matlab, Thomson Course Technology, 2004
[31] J.H. Lai, P.C Yuen, W.S. Chen, S. Lao and M. Kawade, “Robust Facial Feature Point Detection Under Nonlinear Illuminations,” in Proc. of IEEE ICCV Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-time Systems, Canada, 2001, pp.168-174.
[32] J. K. Kamarainen and H. Kalviainen, “Invariance Properties of Gabor Filter-Based Features---Overview and Application,” IEEE Trans. on Image Processing, vol. 15, no 5, pp.1088-1099, 2006.
[33] L. Wiskott, J. M. Fellous, N. Kruger and von der Malsburg, “Face Recognition by Elastic Bunch Graph Matching,” IEEE Trans. on Pattern analysis and Machine Intelligence, vol. 19, no. 7, pp. 775-779, 1997.
[34] E. J. Hokdeb and R. Owens, “Automatic Facial Point Detection,” in Proc. of 5th Asian Conference on Computer Vision, Melbourne, Australia, 2002, pp. 731-736.
[35] D. Vukadinovic and M. Pantic, “Fully Automatic Facial Feature Point Detection Using Gabor Feature Based Boosted Classifiers,” in Proc. of IEEE Int. Conf. on Systems, Man and Cybernetics, Hawaii, U.S.A., 2005, pp. 1692 – 1698.
73
[36] Y. Zhang and Q. Ji, “Active and Dynamic Information Fusion for Facial Expression Understanding from Image Sequences,” IEEE Trans. on Pattern Analysis and Machine Intelligent, vol. 27, no 5, pp. 699-714, 2005
[37] http://cobweb.ecn.purdue.edu/~aleix/aleix_face_DB.html [38] IC-MEDIA Corp.: ICM205B Datasheet, Oct 2002.
[39] Henrry Andrian and K. T. Song, “Embedded CMOS Imaging System for Real-Time Robotic Vision,” in Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alberta, Canada, 2005, pp. 3694-3699.
[40] http://www.pololu.com/products/pololu/0727/
[41] J.H. Hsu: Bimodal Emotion Recognition System Using Image and Speech Information, Master. Eng. Thesis, National Chiao Tung University, Hsinchu, Taiwan, R.O.C., 2006.