Abstract
In this paper, we propose a novel approach for facial expression analysis and recognition. The main contributions of the paper are as follows. First, we propose an efficient facial expression recognition scheme based on the detection of keyframes in videos where the recognition is performed using a temporal classifier. Second, we use the proposed method for extending the human-machine interaction functionality of the AIBO robot. More precisely, the robot is displaying an emotional state in response to the recognized user’s facial expression. Experiments using unseen videos demonstrated the effectiveness of the developed method.
This work was supported by the MEC project TIN2005-09026 and The Ramón y Cajal Program.
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Fasel, B., Luettin, J.: Automatic facial expression analysis: A survey. Pattern Recognition 36(1), 259–275 (2003)
Yeasin, M., Bullot, B., Sharma, R.: Recognition of facial expressions and measurement of levels of interest from video. IEEE Transactions on Multimedia 8(3), 500–508 (2006)
Cañamero, L., Gaussier, P.: Emotion understanding: robots as tools and models. In: Emotional Development: Recent Research Advances, pp. 235–258 (2005)
Picard, R.W., Vyzas, E., Healy, J.: Toward machine emotional intelligence: Analysis of affective physiological state. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(10), 1175–1191 (2001)
Tian, Y., Kanade, T., Cohn, J.F.: Recognizing action units for facial expression analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 23, 97–115 (2001)
Bartlett, M., Littlewort, G., Lainscsek, C., Fasel, I., Movellan, J.: Machine learning methods for fully automatic recognition of facial expressions and facial actions. In: IEEE Int. Conference on Systems, Man and Cybernetics, pp. 592–597 (2004)
Sung, J., Lee, S., Kim, D.: A real-time facial expression recognition using the STAAM. In: International Conference on Pattern Recognition, pp. 275–278 (2006)
Cohen, I., Sebe, N., Garg, A., Chen, L., Huang, T.S.: Facial expression recognition from video sequences: Temporal and static modeling. Computer Vision and Image Understanding 91(1-2), 160–187 (2003)
Black, M.J., Yacoob, Y.: Recognizing facial expressions in image sequences using local parameterized models of image motion. International Journal of Computer Vision 25(1), 23–48 (1997)
Rabiner, L.: A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE 77(2), 257–286 (1989)
Zhang, Y., Ji, Q.: Active and dynamic information fusion for facial expression understanding from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(5), 699–714 (2005)
Ahlberg, J.: CANDIDE-3 - an updated parametrized face. Technical Report LiTH-ISY-R-2326, Department of Electrical Engineering, Linköping University, Sweden (2001)
Dornaika, F., Davoine, F.: On appearance based face and facial action tracking. IEEE Transactions on Circuits and Systems for Video Technology 16(9), 1107–1124 (2006)
Dornaika, F., Raducanu, B.: Recognizing facial expressions in videos using a facial action analysis-synthesis scheme. In: IEEE International Conference on Advanced Video and Signal based Surveillance, IEEE Computer Society Press, Los Alamitos (2006)
Breazeal, C., Scassellati, B.: Robots that imitate humans. Trends in Cognitive Science 6, 481–487 (2002)
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Dornaika, F., Raducanu, B. (2007). Efficient Facial Expression Recognition for Human Robot Interaction . In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_84
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DOI: https://doi.org/10.1007/978-3-540-73007-1_84
Publisher Name: Springer, Berlin, Heidelberg
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