Recognition of facial expressions - Knowledge Based Systems ...

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- renormalize the w ijk to assure that: o = 1 j w ijk o 0 w ≤ 1 < ijk Learning in Bayes Nets There are several variants for learning in BBN. The network structure might be known or unknown. In addition to this, the training examples might provide values of all network variables, or just a part. If the structure is known and the variables are partially observable, the learning procedure in BBN is similar to training neural network with hidden units. By using gradient ascent, the network can learn conditional probability tables. The mechanism is converging to the network h that locally maximizes P(D | h). Complexity - The computational complexity is exponential in the size of the loop cut set, as we must generate and propagate a BBN for each combination of states of the loop cut set. - The identification of the minimal loop cut set of a BBN is NP-hard, but heuristic methods exist to make it feasible. - The computational complexity is a common problem to all methods moving from polytrees to multiply connected graphs. Advantages - Capable of discovering causal relationships - Has probabilistic semantics for fitting the stochastic nature of both the biological processes & noisy experimentation - 38 -
Disadvantages - Can’t deal with the continuous data - In order to deal with temporal expression data, several changes have to be done For the current project experiments, the class for managing the BBN is represented in Listing 1. The reasoning mechanism is built on the base of the already made SMILE BBN library. //-------------------------- 1: class model 2: { 3: DSL_network net; 4: x_line l[500]; 5: int nl; 6: int NP; 7: public: 8: bool read(char*); 9: void set_Param(int); 10: void test(void); 11: int testOne(int); 12: }; //-------------------------- Listing 1. BBN C++ class Each data sample in the model is stored in the structure presented in Listing 2. //-------------------------- 1: struct x_line 2: { 3: int param[20]; 4: char exp[50]; 5: }; //-------------------------- Listing 2. Structure for storing a data sample The C++ source for the BNN routines, according to conducted experiments, is presented in Appendix. - 39 -
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 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
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