Recognition of facial expressions - Knowledge Based Systems ...

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Once executed, the tool recurrently searched for files having the extension “KH” in a specified directory given as call parameter for the console application. For each file found, the application loads the content into the memory by storing the coordinates of the FCPs. For each sample, it further applies a set of computations in order to determine the value of the parameters (Table 7), given the adopted processing model (Figure 17). For the conducted experiment, no dynamic parameters were involved, since there were no data concerning the temporal variability available. However, the initial design included a general functional model that consisted also in a set of dynamic characteristics. For the results presented in the project report, there were also no values encoding the behavior of the parameters related to the forehead, chin and nasolabial areas. Instead, an additional set of experiments was run for analyzing the influence of those parameters. Table 7. The set of parameters and the corresponding facial features The values of the parameters were initially considered real numbers. After the discretization process was finished, all the parameter values consisted in integer values. - 70 -
Figure 17. The facial areas involved in the feature extraction process The discretization process started after all the FCP files were loaded into the memory. For each parameter, the system searched for the minimum and maximum value. Then the values were used to create an interval that includes all the sample values. Given the number of distinct classes to exist following the discretization process, the interval was split in a number of pieces equal to that of the classes (Figure 18). Figure 18. Discretization process The result of the discretization process is presented in Listing 4. //---------------------------------------------------------------------------------------------- 1: 486 10 7 2: 10 001 1+2+20+21+25 3 4 3 5 1 1 3 2 3 5 3: 10 002 1+2+5+25+27 4 5 5 7 4 3 3 5 6 2 4: 10 003 4+17 2 4 2 5 3 1 3 2 2 3 5: 10 004 4+7e+17d+23d+24d 2 3 2 3 1 1 3 2 2 2 6: 10 005 4+6+7+9e+16+25 1 3 2 3 1 1 2 3 4 2 7: 10 006 6+12+16c+25 2 3 3 5 2 1 7 1 1 5 8: ------------------------------------------------------------------------- 9: 11 001 1+2+25+27 4 6 2 6 3 3 4 6 6 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476: 137 002 12b 5 4 2 3 2 2 3 3 2 3 477: 137 003 25 6 4 1 4 3 3 3 3 3 3 478: 137 004 25+26 5 4 1 4 2 2 2 4 3 3 479: ------------------------------------------------------------------------- 480: 138 001 1+4+5+10+20+25+38 4 3 2 4 3 1 3 3 3 5 481: 138 002 5+20+25 3 3 2 5 4 3 3 3 3 4 482: 138 003 25+27 3 3 2 4 2 2 3 4 4 2 - 71 -
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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
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: [image width] [image height] ---- A
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
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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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