Recognition of facial expressions - Knowledge Based Systems ...

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These parameters are used as input to classifiers based on Bayesian Beliaf Networks, neural networks or other classifiers to recognize upper facial action units and all their possible combinations. The base for the expression recognition engine is supported through a BBN model that also handles the time behavior of the visual features. On a completely natural dataset with lots of head movements, pose changes and occlusions (Cohn-Kanade AU-coded facial expression database), the new probabilistic framework (based on BBN) achieved a recognition accuracy of 68 %. Other experiments implied the use of Linear Vector Quantization (LVQ) method, Probabilistic Neural Networks or Back-Prop Neural Networks. The results can be seen in the Experiments section of the report. There are some items on the design of the project that have not been fully covered yet. Among them, the most important is the inclusion of temporal-based parameters to be used in the recognition process. At the moment of running the experiments there were no data available on the dynamic behavior of the model parameters. However, the dynamic aspects of the parameters constitutes subject to further research in the field of facial expression recognition. - 112 -
REFERENCES Almageed, W. A., M. S. Fadali, G. Bebis, ‘A non-intrusive Kalman Filter-Based Tracker for Pursuit Eye Movement’, Proceedings of the 2002 American Control Conference Alaska, 2002 Azarbayejani, A., A. Pentland ‘Recursive estimation of motion, structure, and focal length’ IEEE Trans. PAMI, 17(6), 562-575, June 1995 Baluja, S., D. Pomerleau, ‘Non-intrusive Gaze Tracking Using Artificial Neural Networks’, Technical Report CMU-CS-94-102. Carnegie Mellon University, 1994 Bartlett, M. S., 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 Bartlett, M. S., G. Littlewort, C. Lainscsek, I. Fasel, J. Movellan, ‘Machine learning methods for fully automatic recognition of facial expressions and facial actions’, Proceedings of IEEE SMC, pp. 592–597, 2004 Bartlett, M. S., G. Littlewort, I. Fasel, J. R. Movellan, ‘Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction’, CVPR’03, 2003 Bartlett, M. A., J. C. Hager, P. Ekman, T. Sejnowski, ‘Measuring facial expressions by computer image analysis’, Psychophysiology, 36(2):253–263, March, 1999 Black, M., Y. Yacoob, ‘Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion’, Intel. J. of Computer Vision, 25(1), pp. 23-48, 1997 Black, M., Y. Yacoob, ‘Tracking and recognizing rigid and non-rigid facial motions using local parametric model of image motion’, In Proceedings of the International Conference on Computer Vision, pages 374–381, Cambridge, MA, IEEE Computer Society, 1995 Bourel, F., C. C. Chibelushi, A. A. Low, ‘Recognition of Facial Expressions in conditions of occlusion’, BMVC’01, pp. 213-222, 2001 Brown, R., P. Hwang, ‘Introduction to Random Signals and Applied Kalman Filtering’, 3rd edition, Wiley, 1996 Brunelli, R., T. Poggio, ‘Face recognition: Features vs. templates’, IEEE Trans. Pattern Analysis and Machine Intelligence, 15(10):1042–1053, 1993
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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
Page 61 and 62: SMILE resides in a dynamic link lib
Page 63 and 64: FCP Management Application The Cohn
Page 65 and 66: Figure 13. Head rotation in the ima
Page 67 and 68: Table 6. The set of rules for the u
Page 69 and 70: [image width] [image height] ---- A
Page 71 and 72: Figure 17. The facial areas involve
Page 73 and 74: The functionality of the tool was b
Page 75 and 76: A small part of the output text fil
Page 77 and 78: o call a specialized routine for co
Page 79 and 80: There is another kind of structure
Page 81 and 82: performing classification of facial
Page 83 and 84: Figure 22. Sobel edge detector appl
Page 85 and 86: almost closed it obviously does not
Page 87 and 88: Figure 28. FCP detection The effici
Page 89 and 90: TESTING AND RESULTS The following s
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
Page 103 and 104: PCA experiment “Principal Compone
Page 105 and 106: Eigenvalues: Eigenvectors: Factor l
Page 107 and 108: Squared cosines of the variables: C
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Page 111: CONCLUSION The human face has attra
Page 115 and 116: Essa, A. Pentland, ‘Coding, analy
Page 117: Samal, A., P. Iyengar, ‘Automatic
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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 and 132: detailed in table 4. The dependency
Page 133: [11] M. Turk, A. Pentland ‘Face r
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