Recognition of facial expressions - Knowledge Based Systems ...

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Figure 2. Facial characteristic points model The computation of the eigenvectors was done offline as a preliminary step of the process. For each input image, the first processing stage extracts the image data according to the three areas. Each data image is projected through the eigenvectors and the pattern with the minimum error is searched. The label of the extracted pattern is then fed to the quantification function for obtaining the characteristic output value of each image area. Each value is further set as evidence in the probabilistic BBN. Figure 3. Examples of patterns used in PCA recognition - 22 -
Model parameters The Bayesian Belief Network encodes the knowledge of the existent phenomena that triggers changes in the aspect of the face. The model does include several layers for the detection of distinct aspects of the transformation. The lowest level is that of primary parameter layer. It contains a set of parameters that keeps track of the changes concerning the facial key points. Those parameters may be classified as static and dynamic. The static parameters handle the local geometry of the current frame. The dynamic parameters encode the behavior of the key points in the transition from one frame to another. By combining the two sorts of information, the system gets a high efficiency of expression recognition. An alternative is that the base used for computing the variation of the dynamic parameters is determined as a previous tendency over a limited past time. Each parameter on the lowest layer of the BBN has a given number of states. The purpose of the states is to map any continuous value of the parameter to a discrete class. The number of states has a direct influence on the efficiency of recognition. The number of states for the low-level parameters does not influence the time required for obtaining the final results. It is still possible to have a real time implementation even when the number of states is high. Table 1. The used set of Action Units - 23 -
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 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
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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
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