Recognition of facial expressions - Knowledge Based Systems ...

More documents

Recommendations

Info

Figure 15. FCP Annotation Application For the all set of images from the database, there has been obtained an equal number of FCP set files (“KH”) (Figure 16). Figure 16. The preprocessing of the data samples that implies FCP anotation The format of 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 of 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 of text files including the specification of 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 of the application consisted in performing the discretization process for the value of each of the parameters, for all the input samples. - 69 -
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 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
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
Page 120 and 121:
61: } 62: } 63: float model::comput
Page 122 and 123:
119: for(k=0;k
Page 125 and 126:
APPENDIX B Datcu D., Rothkrantz L.J
Page 127 and 128:
facial feature and store the inform
Page 129 and 130:
available as part of the knowledge
Page 131 and 132:
detailed in table 4. The dependency
Page 133:
[11] M. Turk, A. Pentland ‘Face r
show all

Recognition of facial expressions - Knowledge Based Systems ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?