Abstract
This paper proposes a new facial expression recognition method which combines Higher Order Local Autocorrelation (HLAC) features with Weighted PCA. HLAC features are computed at each pixel in the human face image. Then these features are integrated with a weight map to obtain a feature vector. We select the weight by combining statistic method with psychology theory. The experiments on the “CMU-PITTSBURGH AU-Coded Face Expression Image Database” show that our Weighted PCA method can improve the recognition rate significantly without increasing the computation, when compared with PCA.
Keywords
- Facial Expression
- Recognition Rate
- Facial Expression Recognition
- Principal Component Analysis Method
- Face Action Code System
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
Mehrabian, A.: Communication without words. Psychology Today 2(4), 53–56 (1968)
Guoliang, Y., Zhiliang, W., Jingxia, R.: Facial expression recognition based on adaboost algorithm. Computer Applications 25(4) (April 2005)
Lisetti, C.L., Schiano, D.J.: Automatic Facial Expression Interpretation: Where Human-Computer Interaction, Arti cial Intelligence and Cognitive Science Intersect. Pragmatics and Cognition (Special Issue on Facial Information Processing: A Multidisciplinary Perspective) 8(1), 185–235 (2000)
Alldrin, N., Smith, A., Turnbull, D.: Classifying Facial Expression with Radial Basis Function Networks, using Gradient Descent and K-means. CSE253 (2003)
Cohen, I., Sebe, N., Cozman, F.G.: Learning Bayesian network classifiers for facial expression recognition using both labeled and unlabeled data. In: CVPR 2003, vol. I, p. 595 (2003)
Kurita, T., Otsu, N., Sato, T.: A Face Recognition Method Using Higher Order Local Autocorrelation and Multivariate Analysis. In: Proc. IAPR Int. Conf. on Pattern Recognition, pp. 213–216 (1992)
Yu, Q., Xiyue, H., Yi, C.: Face recognition based on Weighted PCA. Journal of Chongqing University 27(3) (March 2004)
Turk, M., Pentland, A.: Eigen Faces for Recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)
Ekman, P., et al.: Facial Action Coding System. Consulting Psychologists Press, Palo Alto (1978)
Michel, P., EI Kaliouby, R.: Real time Facial Expression Recognition in Video using Support Vector Machines. In: ICMI 2003, November 5-7 (2003)
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© 2005 Springer-Verlag Berlin Heidelberg
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Liu, F., Wang, Zl., Wang, L., Meng, Xy. (2005). Facial Expression Recognition Using HLAC Features and WPCA. In: Tao, J., Tan, T., Picard, R.W. (eds) Affective Computing and Intelligent Interaction. ACII 2005. Lecture Notes in Computer Science, vol 3784. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11573548_12
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DOI: https://doi.org/10.1007/11573548_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29621-8
Online ISBN: 978-3-540-32273-3
eBook Packages: Computer ScienceComputer Science (R0)