Recognition of facial expressions - Knowledge Based Systems ...

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List of figures Figure 1. Kobayashi & Hara 30 FCPs model ...............................................................- 21 - Figure 2. Facial characteristic points model .................................................................- 22 - Figure 3. Examples of patterns used in PCA recognition.............................................- 22 - Figure 4. BBN used for facial expression recognition..................................................- 28 - Figure 5. AU classifier discovered structure.................................................................- 29 - Figure 6. Dominant emotion in the sequence ...............................................................- 30 - Figure 7. Examples of expression recognition applied on video streams.....................- 30 - Figure 8. Simple model for facial expression recognition............................................- 34 - Figure 9. PCA image recognition and emotion assignment .........................................- 41 - Figure 10. Mapping any value so as to be encoded by a single neuron........................- 46 - Figure 11. IR-adapted web cam....................................................................................- 55 - Figure 12. The dark-bright pupil effect in infrared.......................................................- 56 - Figure 13. Head rotation in the image ..........................................................................- 65 - Figure 14. The ZOOM-IN function for FCP labeling...................................................- 66 - Figure 15. FCP Management Application ....................................................................- 68 - Figure 16. The preprocessing of the data samples that implies FCP anotation ............- 68 - Figure 17. The facial areas involved in the feature extraction process.........................- 71 - Figure 18. Discretization process..................................................................................- 71 - Figure 19. System functionality....................................................................................- 80 - Figure 20. The design of the system .............................................................................- 81 - Figure 21. Initial IR image............................................................................................- 82 - Figure 22. Sobel edge detector applied on the initial image.........................................- 83 - Figure 23. Threshold applied on the image ..................................................................- 83 - Figure 24. The eye-area searched .................................................................................- 84 - Figure 25. The eyes area found.....................................................................................- 84 - Figure 26. Model 's characteristic points ......................................................................- 85 - Figure 27. Characteristic points area ............................................................................- 86 - Figure 28. FCP detection .............................................................................................- 87 - Figure 29. The response of the system.........................................................................- 88 - VIII
List of tables Table 1. The used set of Action Units...........................................................................- 23 - Table 2. The set of visual feature parameters ...............................................................- 24 - Table 3. The dependency between AUs and intermediate parameters .........................- 25 - Table 4. The emotion projections of each AU combination.........................................- 28 - Table 5. 3x3 window enhancement filters ....................................................................- 53 - Table 6. The set of rules for the uniform FCP annotation scheme ...............................- 67 - Table 7. The set of parameters and the corresponding facial features..........................- 70 - Table 8. Emotion predictions........................................................................................- 73 - IX
Page 1 and 2: Automatic recognition of facial exp
Page 3 and 4: Man-Machine Interaction Group Facul
Page 5 and 6: Acknowledgements The author would l
Page 7: Eye Detection Module ..............
Page 12 and 13: data taken from the Cohn-Kanade AU-
Page 14 and 15: - The discussions on the current ap
Page 16 and 17: ecognition in static pictures, for
Page 18 and 19: In [Wang and Tang, 2003] the author
Page 20 and 21: Data preparation Starting from the
Page 22 and 23: Figure 2. Facial characteristic poi
Page 24 and 25: The only additional time is that of
Page 26 and 27: African-American and three percent
Page 28 and 29: Table 4. The emotion projections of
Page 30 and 31: contains a large sample of varying
Page 32 and 33: Bayesian networks were designed to
Page 34 and 35: - correctly identify the goals of m
Page 36 and 37: In the final step of constructing a
Page 38 and 39: - renormalize the w ijk to assure t
Page 40 and 41: Principal Component Analysis The ne
Page 42 and 43: The term σ ij is the covariance be
Page 44 and 45: T The term rank( X ∗ X ) is gener
Page 46 and 47: numeric information. Usually, a neu
Page 48 and 49: defined as: ∆w = −η ∇ ji ji
Page 50 and 51: ∂E ∂a j = i E ∂a pk ∂a pk
Page 52 and 53: Spatial Filtering The spatial filte
Page 54 and 55: way high pass filter were used for
Page 56 and 57: 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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performing classification of facial
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Figure 22. Sobel edge detector appl
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almost closed it obviously does not
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Figure 28. FCP detection The effici
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TESTING AND RESULTS The following s
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BBN experiment 2 “Detection of fa
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General recognition rate is 63.77%
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Recognition results. Confusion Matr
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5 states model General recognition
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LVQ experiment “LVQ based facial
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ANN experiment Back Propagation Neu
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PCA experiment “Principal Compone
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Eigenvalues: Eigenvectors: Factor l
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Squared cosines of the variables: C
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- 109 -
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CONCLUSION The human face has attra
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REFERENCES Almageed, W. A., M. S. F
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Essa, A. Pentland, ‘Coding, analy
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Samal, A., P. Iyengar, ‘Automatic
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61: } 62: } 63: float model::comput
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119: for(k=0;k
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APPENDIX B Datcu D., Rothkrantz L.J
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facial feature and store the inform
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available as part of the knowledge
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detailed in table 4. The dependency
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[11] M. Turk, A. Pentland ‘Face r
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