Recognition of facial expressions - Knowledge Based Systems ...

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Automatic recognition of facial expressions Master’s Thesis in Media & Knowledge Engineering Man-Machine Interaction Group Faculty of Electrical Engineering, Mathematics, and Computer Science Delft University of Technology Dragos Datcu 1138758 November 2004 II
Man-Machine Interaction Group Faculty of Electrical Engineering, Mathematics, and Computer Science Delft University of Technology Mekelweg 4 2628 CD Delft The Netherlands Members of the Supervising Committee drs. dr. L.J.M. Rothkrantz prof. dr. H. Koppelaar prof. dr. ir. E.J.H. Kerckhoffs ir. F. Ververs III
Page 1: Automatic recognition of facial exp
Page 5 and 6: Acknowledgements The author would l
Page 7 and 8: Eye Detection Module ..............
Page 9: List of tables Table 1. The used se
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
Page 59 and 60:
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
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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
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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
Page 83 and 84:
Figure 22. Sobel edge detector appl
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almost closed it obviously does not
Page 87 and 88:
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
Page 99 and 100:
LVQ experiment “LVQ based facial
Page 101 and 102:
ANN experiment Back Propagation Neu
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PCA experiment “Principal Compone
Page 105 and 106:
Eigenvalues: Eigenvectors: Factor l
Page 107 and 108:
Squared cosines of the variables: C
Page 109 and 110:
- 109 -
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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Essa, A. Pentland, ‘Coding, analy
Page 117:
Samal, A., P. Iyengar, ‘Automatic
Page 120 and 121:
61: } 62: } 63: float model::comput
Page 122 and 123:
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
Page 133:
[11] M. Turk, A. Pentland ‘Face r
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