Recognition of facial expressions - Knowledge Based Systems ...

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Principal Component Analysis The need for the PCA technique came from the fact that it was necessary to have a classification mechanism for handling special areas on the surface of the face. There were three areas of a high importance for the analysis. The first is the area between the eyebrows. For instance, the presence of wrinkles in that area can be associated to the tension in facial muscles ‘Corrugator supercilii’/’Depressor supercilii’ and so presence of Action Unit 4, for the ‘lowered brow’ state. The second area is the nasolabial area. There are certain facial muscles whose changes can produce the activation of certain Action Units in the nasolabial area. The Action Unit 6 can be triggered by tension in facial muscle ‘Orbicularis oculi, pars orbitalis’. In the same way, tension in facial muscle ‘Levator labii superioris alaquae nasi’ can activate Action Unit 9 and strength of facial muscle ‘Levator labii superioris’ can lead to the activation of Action Unit 10. The last visual area analyzed through the image processing routines is that of the chin. The tension in the facial muscle ‘Mentalis’ is associated to the presence of Action Unit 17, ‘raised chin’ state. The PCA technique was used to process the relatively large images for the described facial areas. The size of each area was expressed in terms of relative value comparing to the distance between the pupils. That was used for making the process robust to the distance the person stands from the camera, and person-independent. The analyze was done separately, for each facial area. Every time there was available a set of 485 n-size vectors where n equals the width of the facial area multiplied by the height. In the common case, the size of one sample vector is of the order of few thousand values, one value per pixel. The facial image space was highly redundant and there were large amounts of data to be processed for making the classification of the desired emotions. Principal Components Analysis (PCA) is a statistical procedure which rotates the data such that maximum variability is projected onto the axes. Essentially, a set of correlated variables are transformed into a set of uncorrelated variables which are ordered by reducing variability. The uncorrelated variables are linear combinations of the original variables, and the last of these variables can be removed with minimum loss of real data. - 40 -
The main use of PCA was to reduce the dimensionality of the data set while retaining as much information as is possible. It computed a compact and optimal description of the facial data set. Figure 9. PCA image recognition and emotion assignment The dimensionality reduction of data was done by analyzing the covariance matrix Σ . The reason, for which the facial data is redundant, is fact that each pixel in a face is highly correlated to the other pixels. The covariance matrix σ ij , for an image set is highly non-diagonal: Equation 8 σ = X ∗ X ij T = σ σ σ X 11 X 21 ... X w* h,1 σ σ σ X 12 X 22 ... X w* h,2 ... ... ... ... σ σ σ X 1, w* h X 2, w* h ... X w* h, w* h - 41 -
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 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
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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
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
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Samal, A., P. Iyengar, ‘Automatic
Page 120 and 121:
61: } 62: } 63: float model::comput
Page 122 and 123:
119: for(k=0;k
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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
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