Figure 15. FCP Annotation Application For the all set <strong>of</strong> images from the database, there has been obtained an equal number <strong>of</strong> FCP set files (“KH”) (Figure 16). Figure 16. The preprocessing <strong>of</strong> the data samples that implies FCP anotation The format <strong>of</strong> the output text file is given line by line, as: ---- a text string (“K&H.enhanced 36 points”) - 68 -
[image width] [image height] ---- An example <strong>of</strong> an output file is given below K&H.enhanced 36 points 640 490 348 227 407 226 p1:349,226 p2:407,227 p3:289,225 p4:471,224 p5:319,229 p6:437,228 p7:319,210 p8:437,209 p9:303,226 p10:457,225 p11:335,226 p12:421,227 p13:303,213 p14:457,213 p15:335,217 p16:421,217 p17:319,186 p18:437,179 p19:352,201 p20:403,198 p21:343,200 p22:412,196 p23:324,341 p24:440,343 p25:378,373 p26:378,332 p27:360,366 p28:396,368 p29:360,331 p30:396,328 p31:273,198 p32:485,193 p33:378,311 p34:378,421 p35:292,294 p36:455,289 Parameter Discretization By using the FCP Management Application, all the images from the initial Cohn-Kanade AU-Coded Facial Expression Database were manually processed and a set <strong>of</strong> text files including the specification <strong>of</strong> Facial Characteristic Point locations has been obtained. The Parameter Discretization Application was further used for analyzing all the “KH” files previously created and to gather all the data in a single output text file. An important task <strong>of</strong> the application consisted in performing the discretization process for the value <strong>of</strong> each <strong>of</strong> the parameters, for all the input samples. - 69 -
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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
- 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: Table 6. The set of rules for the u
- 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
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- 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
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- 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