Recognition of facial expressions - Knowledge Based Systems ...

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Kanade AU-Coded Facial Expression Database for building the system knowledge. It contains a large sample of varying age, sex and ethnic background and so the robustness to the individual changes in facial features and behavior is high. The BBN model takes care of the variation and degree of uncertainty and gives us an improvement in the quality of recognition. As off now, the results are very promising and show that the new approach presents high efficiency. An important contribution is related to the tracking of the temporal behavior of the analyzed parameters and the temporal expression constrains. Figure 6. Dominant Emotional expression in sequence Figure 7. Example of facial expression recognition applied on video streams References [1] W. A-Almageed, M. S. Fadali, G. Bebis ‘A nonintrusive Kalman Filter-Based Tracker for Pursuit Eye Movement’ Proceedings of the 2002 American Control Conference Alaska, 2002 [2] M. S. Bartlett, G. Littlewort, I. Fasel, J. R. Movellan ‘Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction’ IEEE Workshop on Face Processing in Video, Washington 2004 [3] I. Cohen, N. Sebe, A. Garg, M. S.Lew, T. S. Huang ‘Facial expression recognition from video sequences’ Computer Vision and Image Understanding, Volume 91, pp 160 - 187 ISSN: 1077-3142 2003 [4] D. Datcu, L. J. M. Rothkrantz ‘A multimodal workbench for automatic surveillance’ Euromedia Int’l Conference 2004 [5] M. J. Druzdzel ‘GeNIe: A development environment for graphical decision-analytic models’. In Proceedings of the 1999 Annual Symposium of the American Medical Informatics Association (AMIA-1999), page 1206, Washington, D.C., November 6-10, 1999 [6] P. Ekman, W. V. Friesen ‘Facial Action Coding System: Investigator’s Guide’ Consulting Psychologists Press, 1978 [7] E. J. de Jongh, L .J. M. Rothkrantz ‘FED – an online Facial Expression Dictionary’ Euromedia Int’l Conference 2004 [8] T. Kanade, J. Cohn, Y. Tian ‘Comprehensive database for facial expression analysis’ Proc. IEEE Int’l Conf. Face and Gesture Recognition, pp. 46-53, 2000 [9] H. Kobayashi and F. Hara. ‘Recognition of Mixed Facial Expressions by Neural Network’ IEEE International workshop on Robot and Human Communication, 381-386, 1972 [10] M. Pantic, L. J. M. Rothkrantz ‘Toward an Affect- Sensitive Multimodal Human-Computer Interaction’ IEEE proceedings vol. 91, no. 9, pp. 1370-1390, 2003 132
[11] M. Turk, A. Pentland ‘Face recognition using eigenfaces, Proc. CVPR, pp. 586-591 (1991) of the 2003 ACM SIGMM Berkley, California 2003 [12] X. Wang, X. Tang ‘Bayesian Face Recognition Using Gabor Features’ Proceedings 133
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Automatic recognition of facial exp
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Man-Machine Interaction Group Facul
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Acknowledgements The author would l
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Eye Detection Module ..............
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List of tables Table 1. The used se
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data taken from the Cohn-Kanade AU-
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- The discussions on the current ap
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ecognition in static pictures, for
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In [Wang and Tang, 2003] the author
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Data preparation Starting from the
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Figure 2. Facial characteristic poi
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The only additional time is that of
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African-American and three percent
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Table 4. The emotion projections of
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contains a large sample of varying
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Bayesian networks were designed to
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- correctly identify the goals of m
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In the final step of constructing a
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- renormalize the w ijk to assure t
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Principal Component Analysis The ne
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The term σ ij is the covariance be
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T The term rank( X ∗ X ) is gener
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numeric information. Usually, a neu
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defined as: ∆w = −η ∇ ji ji
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∂E ∂a j = i E ∂a pk ∂a pk
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Spatial Filtering The spatial filte
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way high pass filter were used for
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module includes some routines for d
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IMPLEMENTATION Facial Feature Datab
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SMILE resides in a dynamic link lib
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FCP Management Application The Cohn
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Figure 13. Head rotation in the ima
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Table 6. The set of rules for the u
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[image width] [image height] ---- A
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Figure 17. The facial areas involve
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The functionality of the tool was b
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A small part of the output text fil
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o call a specialized routine for co
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There is another kind of structure
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Page 91 and 92: BBN experiment 2 “Detection of fa
Page 93 and 94: General recognition rate is 63.77%
Page 95 and 96: Recognition results. Confusion Matr
Page 97 and 98: 5 states model General recognition
Page 99 and 100: LVQ experiment “LVQ based facial
Page 101 and 102: ANN experiment Back Propagation Neu
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Page 105 and 106: Eigenvalues: Eigenvectors: Factor l
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Page 111 and 112: CONCLUSION The human face has attra
Page 113 and 114: REFERENCES Almageed, W. A., M. S. F
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Page 117: Samal, A., P. Iyengar, ‘Automatic
Page 120 and 121: 61: } 62: } 63: float model::comput
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Page 125 and 126: APPENDIX B Datcu D., Rothkrantz L.J
Page 127 and 128: facial feature and store the inform
Page 129 and 130: available as part of the knowledge
Page 131: detailed in table 4. The dependency
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