Recognition of facial expressions - Knowledge Based Systems ...

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Table 4. The emotion projections of each AU combination Figure 4. BBN used for facial expression recognition For one of the conducted experiments, GeNIe Learning Tool was used for doing structure learning based on the data. The tool was configured so as to take into account the next learning rules: - discovery of causal relationships had to be done only among the parameters representing the Action Units and those representing the model parameters - causal relationships among AU parameters did not exist - causal relationships among model parameters did not exist - all the causal relationships among the parameter representing the emotional expressions and the others, representing the model parameters, were given The structure of the resulted Bayesian network is presented in Figure 5. The associated emotion recognition rate was 63.77 %. - 28 -
Figure 5. AU classifier discovered structure The implementation of the model was made using C/C++ programming language. The system consists in a set of applications that run different tasks that range from pixel/image oriented processing to statistics building and inference by updating the probabilities in the BBN model. The support for BBN was based on S.M.I.L.E. (Structural Modeling, Inference, and Learning Engine), a platform independent library of C++ classes for reasoning in probabilistic models [M.J.Druzdzel 1999]. S.M.I.L.E. is freely available to the community and has been developed at the Decision Systems Laboratory, University of Pittsburgh. The library was included in the AIDPT framework. The implemented probabilistic model is able to perform recognition on six emotional classes and the neutral state. By adding new parameters on the facial expression layer, the expression number on recognition can be easily increased. Accordingly, new AU dependencies have to be specified for each of the emotional class added. In Figure 7 there is an example of an input image sequence. The result is given by the graphic containing the information related to the probability of the dominant detected facial expression (Figure 6). In the current project the items related to the development of an automatic system for facial expression recognition in video sequences were discussed. As the implementation, a system was made for capturing images with an infra red camera and for processing them in order to recognize the emotion presented by the person. An enhancement was provided for improving the visual feature detection routine by including a Kalman-based eye tracker. The inference mechanism was based on a probabilistic framework. Other kinds of reasoning mechanisms were used for performing recognition. The Cohn-Kanade AU-Coded Facial Expression Database was used for building the system knowledge. It - 29 -
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 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 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
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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 -
Page 111 and 112:
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
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