Recognition of facial expressions - Knowledge Based Systems ...

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Results of the processing Bartlett's sphericity test: Chi-square (observed value) 1887.896 Chi-square (critical value) 61.656 DF 45 One-tailed p-value < 0.0001 Alpha 0.05 Conclusion: At the level of significance Alpha=0.050 the decision is to reject the null hypothesis of absence of significant correlation between variables. It means that the correlation between variables is significant. Mean and standard deviation of the columns: Correlation matrix: In bold, significant values (except diagonal) at the level of significance alpha=0.050 (two-tailed test) - 104 -
Eigenvalues: Eigenvectors: Factor loadings: - 105 -
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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
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
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: PCA experiment “Principal Compone
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
Page 115 and 116: 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
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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