- 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