表情在人與人的溝通中扮演著很重要的角色，最主要是用來傳遞一些情感上面的訊息，是除了語言之外最重要的溝同方式之一。在人機互動越來越流行的現今，要讓電腦知道使用者當下的情緒，利用自動化的表情辨識系統就是最直接且最有用的方式。
表情辨識的難度在於，臉部因為表情產生的變化是一種不規則的變化，而且因為每個人的五官長相和個性都不相同，因此表情的表現方式也會有所差異，這些差異會使得臉部因表情產生的變化分析起來更複雜。
因此在本研究中，我們將表情當成是一個動作去分析，並分析臉部每個區域因為表情而產生的運動方向和大小，藉由這種區域運動方向特徵降低因為人臉長相差異所造成的影響。最後再利用事前訓練好的霍夫隨機森林，來加速這些區域特徵的匹配，讓系統可以透過數位攝影機即時判別使用者的表情動作，以達到自動化即時表情辨識系統。

[1] Mehrabian, “Communication Without Words,. “ Psychology Today, Vol.2, No.4, pp. 53-56, 1968
[2] B. Fasel and J. Luettin, “Automatic facial expression analysis: A survey,” Pattern Recognition, vol. 36, pp. 259-275, Sep. 2003
[3] P. Viola and M. J. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, Dec. 2001, vol. 1, pp. 511-518.
[4] A. Dhall, A. Asthana, R. Goecke, and T. Gedeon. Emotion recognition using PHOG and LPQ features. In Proceedings of the Ninth IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG’2011), Facial Expression Recognition and Analysis Challenge Workshop (FERA), pages 878–883, 2011
[5] C. Shan, S. Gong, and P. McOwan. Facial expression recognition based on local binary patterns: A comprehensive study. Image and Vision Computing, 27(6):803–816, 2008.
[6] G. Zhao, 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) (2007) 915–928.
[7] P. Yang, Q. Liu, D.N. Metaxas, Boosting coded dynamic features for facial action units and facial expression recognition, Computer Vision and Pattern Recognition (2007).
[8] T. F. Cootes, G. J. Edwards, and C. J. Taylor, “Active appearance models,”IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 23, no. 6,pp. 681-685, 2001.
[9] Valstar, M.F. and Pantic, M. Fully automatic facial action unit detection and temporal analysis. In Proc. Int'l Conf. Computer Vision and Pattern Recognition, vol. 3, 149, 2006.
[10] P. Ekman and W. V. Friesen, “Constants across cultures in the face andemotion,” Journal of Personality and Social Psychology, vol. 17, pp.124-129, 1971.
[11] Pantic and L. J.M. Rothkrantz, “Automatic analysis of facial expressions: The state of the art,” IEEE Trans. on Pattern Analysis and achine Intelligence, vol. 22, no. 12, pp. 1424-1445, 2000.
[12] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines. Cambridge University Press, 2000.
[13] S. Yang and B. Bhanu. Facial expression recognition using emotion avatar image. In International Conference on Automatic Face & Gesture Recognition, pages 866 –871,2011.
[14] J. L. Barron, D. J. Fleet, S. S. Beauchemin, and T. A. Burkitt,“Performance of optical flow techniques,” in Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 1992, pp.236-242.
[15] D. Rueckert, L. Sonoda, C. Hayes, D. Hill, M. Leach, and D. Hawkes. Nonrigid registration using free-form deformations: Application to breast mr images. IEEE Transactions on medical imaging, 18(8):712–721, august 1999
[16] S. Koelstra and M. Pantic. Non-rigid registration using freeform deformations for recognition of facial actions and their temporal dynamics. Proc. IEEE Int’l Conf. Automatic Face and Gesture Recognition. 2008.
[17] J. Gall and V. Lempitsky. Class-specific hough forests for object detection. In CVPR, 2009
[18] A. Yao, J. Gall, and L. Van Gool. A hough transform-based voting framework for action recognition. In CVPR, 2010
[20] Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive database for facial expression analysis. In:Automatic Face and Gesture Recognition. (2000) 46–53
[21] Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010). The Extended Cohn-Kande Dataset (CK+): A complete facial expression dataset for action unit and emotion-specified expression. Paper presented at the Third IEEE Workshop on CVPR for Human Communicative Behavior Analysis (CVPR4HB 2010).
[22] Pantic, M., Valstar, M., Rademaker, R., Maat, L.: Web-based database for facial expressionanalysis. In: International Conference on Multimedia and Expo. (2005)
[23] Aleksic, P.S., Katsaggelos, A.K.: Automatic facial expression recognition using facial animation parameters and multi-stream hmms. Trans. on Information Forensics and Security(1)
[24] I. Kotsia, I. Buciu, and I. Pitas, “An analysis of facial expression recognition under partial facial image occlusion,” Image and VisionComputing, vol. 26, no. 7, pp. 1052-1067, July 2008
[25] Yeasin, M., Bullot, B., Sharma, R.: Recognition of facial expressions and measurement of levels of interest from video. Transactions on Multimedia 8 (2006) 500 – 508