Recognition of facial expressions - Knowledge Based Systems ...

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The only additional time is that of processing done for computing the conditioned probability tables for each BBN parameter, but the task is run off-line. Table 2. The set of visual feature parameters The action units examined in the project include facial muscle movements such as inner eyebrow raise, eye widening, and so forth, which combine to form facial expressions. Although prior methods have obtained high recognition rates for recognizing facial action units, these methods either use manually pre-processed image sequences or require human specification of facial features; thus, they have exploited substantial human intervention. According to the method used, each facial expression is described as a combination of existent Action Units (AU). One AU represents a specific facial display. Among 44 AUs contained in FACS, 12 describe contractions of specific facial muscles in the upper part of the face and 18 in the lower part. Table 1 presents the set of AUs that is managed by the current recognition system. An important characteristic of the AUs is that they may act differently in given combinations. According to the behavioral side of each AU, there are additive and non-additive combinations. In that way, the result of one non-additive combination may be related to a - 24 -
facial expression that is not expressed by the constituent AUs taken separately. In the case of the current project, the AU sets related to each expression are split into two classes that specify the importance of the emotional load of each AU in the class. By means of that, there are primary and secondary AUs. The AUs being part of the same class are additive. The system performs recognition of one expression as computing the probability associated with the detection of one or more AUs from both classes. The probability of one expression increases, as the probabilities of detected primary AUs get higher. In the same way, the presence of some AUs from a secondary class results in solving the uncertainty problem in the case of the dependent expression but at a lower level. Table 3. The dependency between AUs and intermediate parameters The conditioned probability tables for each node of the Bayesian Belief Network were filled in by computing statistics over the database. The Cohn-Kanade AU-Coded Facial Expression Database contains approximately 2000 image sequences from 200 subjects ranged in age from 18 to 30 years. Sixty-five percent were female, 15 percent were - 25 -
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 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
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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
Page 109 and 110:
- 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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