Recognition of facial expressions - Knowledge Based Systems ...

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module includes some routines for detecting the position of the edge between the pupil and the iris. The process is based on the characteristic of the dark-bright pupil effect in infrared condition (Figure 12). Figure 12. The dark-bright pupil effect in infrared However, the eye position locator may not perform well in some contexts as poor illuminated scene or the rotation of the head. The same might happen when the person wears glasses or has the eyes closed. The inconvenience is managed by computing the most probable eye position with Kalman filter. The estimation for the current frame takes into account the information related to the motion of the eyes in the previous frames. The Kalman filter relies on the decomposition of the pursuit eye motion into a deterministic component and a random component. The random component models the estimation error in the time sequence and further corrects the position of the eye. It has a random amplitude, occurrence and duration. The deterministic component concerns the motion parameters related to the position, velocity and acceleration of the eyes in the sequence. The acceleration of the motion is modeled as a Gauss-Markov process. The autocorrelation function is as follows: Equation 17 R(t ) = s 2 -b |t| e The equations of the eye movement are defined according to the equation 18. - 56 -
Equation 18 z x& x& x& 1 2 3 0 1 0 = 0 0 1 0 0 − β 2 = [ 2σ β 0 0] x x x x x x 1 2 3 1 2 3 + 0 0 1 u( t) In the model we use, the state vector contains an additional state variable according to the Gauss-Markov process. u(t) is a unity Gaussian white noise. The discrete form of the f t model for tracking the eyes in the sequence is given in Equation 17. φ = e ∆ , w are the process Gaussian white noise and ν is the measurement Gaussian white noise. Equation 19 x z k k = φ + w k = H k ⋅ x k k + v k The Kalman filter method used for tracking the eyes presents a high efficiency by reducing the error of the coordinate estimation task. In addition to that, the process does not require a high processor load and a real time implementation was possible. - 57 -
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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 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 -
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CONCLUSION The human face has attra
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REFERENCES Almageed, W. A., M. S. F
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Essa, A. Pentland, ‘Coding, analy
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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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