Recognition of facial expressions - Knowledge Based Systems ...

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Table of content Abstract............................................................................................................................. IV Acknowledgements............................................................................................................ V Table of content ................................................................................................................ VI List of figures..................................................................................................................VIII List of tables...................................................................................................................... IX INTRODUCTION ........................................................................................................- 11 - Project goal ...............................................................................................................- 13 - LITERATURE SURVEY.............................................................................................- 15 - MODEL ........................................................................................................................- 19 - Material and Method.................................................................................................- 19 - Data preparation....................................................................................................- 20 - Model parameters..................................................................................................- 23 - IR eye tracking module.........................................................................................- 26 - Emotion recognition using BBN...........................................................................- 27 - Bayesian Belief Networks (BBN).............................................................................- 31 - Inference in a Bayesian Network..........................................................................- 36 - Learning in Bayes Nets.........................................................................................- 38 - Complexity............................................................................................................- 38 - Advantages............................................................................................................- 38 - Disadvantages .......................................................................................................- 39 - Principal Component Analysis .................................................................................- 40 - Artificial Neural Networks .......................................................................................- 45 - Back-Propagation..................................................................................................- 45 - Encoding the parameters in the neurons ...............................................................- 45 - Knowledge Building in an ANN...........................................................................- 47 - Encoding ANN......................................................................................................- 50 - Advantages............................................................................................................- 51 - Limitations ............................................................................................................- 51 - Spatial Filtering.........................................................................................................- 52 - Filtering in the Domain of Image Space...............................................................- 52 - Filtering in the domain of Spatial Frequency .......................................................- 53 - Eye tracking ..............................................................................................................- 55 - IMPLEMENTATION...................................................................................................- 59 - Facial Feature Database ............................................................................................- 59 - SMILE BBN library..................................................................................................- 60 - Primary features....................................................................................................- 61 - GeNIe BBN Toolkit..................................................................................................- 61 - Primary Features ...................................................................................................- 62 - GeNIe Learning Wizard component.........................................................................- 62 - FCP Management Application..................................................................................- 63 - Parameter Discretization...........................................................................................- 69 - Facial Expression Assignment Application..............................................................- 72 - CPT Computation Application .................................................................................- 76 - Facial expression recognition application.................................................................- 79 - VI
Eye Detection Module ..........................................................................................- 82 - Face representational model .................................................................................- 87 - TESTING AND RESULTS..........................................................................................- 89 - BBN experiment 1 ....................................................................................................- 90 - BBN experiment 2 ....................................................................................................- 91 - BBN experiment 3 ....................................................................................................- 92 - BBN experiment 4 ....................................................................................................- 94 - BBN experiment 5 ....................................................................................................- 96 - BBN experiment 6 ................................................................................................- 98 - LVQ experiment .......................................................................................................- 99 - ANN experiment.....................................................................................................- 101 - PCA experiment......................................................................................................- 103 - PNN experiment......................................................................................................- 110 - CONCLUSION...........................................................................................................- 111 - REFERENCES ...........................................................................................................- 113 - APPENDIX A.............................................................................................................- 119 - APPENDIX B ................................................................................................................. 125 VII
Page 1 and 2: Automatic recognition of facial exp
Page 3 and 4: Man-Machine Interaction Group Facul
Page 5: Acknowledgements The author would l
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
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 and 104:
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
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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