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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
- Page 69 and 70: [image width] [image height] ---- A
- Page 71 and 72: Figure 17. The facial areas involve
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- 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 and 112: CONCLUSION The human face has attra
- Page 113 and 114: REFERENCES Almageed, W. A., M. S. F
- Page 115 and 116: Essa, A. Pentland, ‘Coding, analy
- Page 117: Samal, A., P. Iyengar, ‘Automatic
- Page 122 and 123: 119: for(k=0;k
- 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