Recognition of facial expressions - Knowledge Based Systems ...

More documents

Recommendations

Info

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
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?