Browse > Article
http://dx.doi.org/10.5302/J.ICROS.2006.12.2.130

New Rectangle Feature Type Selection for Real-time Facial Expression Recognition  

Kim Do Hyoung (한국과학기술원 전자전산학과)
An Kwang Ho (한국과학기술원 전자전산학과)
Chung Myung Jin (한국과학기술원 전자전산학과)
Jung Sung Uk (한국전자통신연구원)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.12, no.2, 2006 , pp. 130-137 More about this Journal
Abstract
In this paper, we propose a method of selecting new types of rectangle features that are suitable for facial expression recognition. The basic concept in this paper is similar to Viola's approach, which is used for face detection. Instead of previous Haar-like features we choose rectangle features for facial expression recognition among all possible rectangle types in a 3${\times}$3 matrix form using the AdaBoost algorithm. The facial expression recognition system constituted with the proposed rectangle features is also compared to that with previous rectangle features with regard to its capacity. The simulation and experimental results show that the proposed approach has better performance in facial expression recognition.
Keywords
rectangle feature; feature selection; facial expression recognition; AdaBoost; pattern classification;
Citations & Related Records
연도 인용수 순위
  • Reference
1 G. C. Littlewort, M. S. Bartlett, J. Chenu, I. Fasel, T. Kanda, H. Ishiguro, & J. R. Movellan, 'Towards social robots: automatic evaluation of human-robot interaction by face detection and expression classification,' In S. Thrun & L. Saul & B. Schoelkopf, (Eds.) Advances in Neural Information Processing Systems, vol 16. pp. 1563-1570, MIT Press, 2004
2 The Japanese Female Facial Expression(JAFFE) Database, http://www.irc.atr.jp/~mlyons/jaffe.htm
3 C. Padgett and G. W. Cottrell, 'Representing face image for emotion classification,' In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Proceessing Systems, vol. 9, pp. 894-900, Cambridge, MA, 1997, MIT Press
4 M. S. Bartlett, 'Face image analysis by unsupervised learning and redundancy reduction,' PhD thesis, Universiy of California, San Diego, 1998
5 J. J. Lien, T. Kanade, J. F. Cohn, cc Li, 'Automated facial expression recognition based F ACS action units,' The Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 390-395, 1998   DOI
6 P. Viola and M. Jones, 'Rapid object detection using a boosted cascade of simple features,' IEEE International Conference on Computer Vision and Pattern Recognition, pp. 511-518, 2001   DOI
7 Y. Wang, H. Ai, B. Wu, C. Huang, 'Real time facial expression recognition with adaboost,' 17th IEEE International Conference on Pattern Recognition, vol. 3, pp. 926-929, 2004   DOI
8 C. L. Huang and Y. M. Huang. 'Facial expression recognition using model-based feature extraction and action parameters calssification,' Journal of Visual Communication and Image Representation, vol. 8, no. 3, pp. 278-290, 1997   DOI   ScienceOn
9 W. A. Fellenz, J. G. Taylor, N. Tsapatsoulis, and S. Kollias, 'Comparing template-based, feature-based and supervised classification of facial expressions form static images,' Circuits, Systems, Communications and Computers, pp. 5331-5336, 1999
10 C. L. Lisetti and D. E. Rumelhart, 'Facial expression recognition using a neural network,' The 11th International Flairs Conference, AAAI Press, 1998
11 Z. Zhang, M. Lyons, M. Schuster, and S. Akamatsu, 'Comparision between geometry-based and gabor-wavelets-based facial expression recognition using multi-layer perceptron,' The Second IEEE International Conference on Automatic Face and Gesture Recognition, pp. 454-459, 1998   DOI
12 P. Ekman and W. V. Friesen 'Unmasking the face,' Malor Books press, 2003
13 B. Fasel, J. Luettin, 'Automatic facial expression analysis: a survey,' Pattern Recognition, vol. 36, no. 1, pp. 259-275, 2003   DOI   ScienceOn
14 J. A. Essa, A. P. Pentland, 'Coding, analysis, interpretation, and recognition of facial expressions,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 757-763, 1997   DOI   ScienceOn
15 A. Lanitis, C. J. Taylor, and T. F. Cootes, 'Automatic interpretation and coding of face images using flexible models,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol 19, no, 7, pp. 743-756, 1997   DOI   ScienceOn
16 M. Pantie, L. J. M. Rothkrantz, 'Automatic analysis of facial expression: the state of art,' IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1424-1445, 2000   DOI   ScienceOn