Recognition of facial expressions - Knowledge Based Systems ...

More documents

Recommendations

Info

∂E ∂a j = i E ∂a pk ∂a pk ∂net pk w kj The second term is known and the first term is computed recursively. Encoding ANN - A minimizing a cost function is performs for mapping the input to output - Cost function minimization • Weight connection adjustments according to the error between computed and desired output values • Usually it is the squared error § Squared difference between computed and desired output values across all patterns in the data set • other cost functions § Entropic cost function • White (1988) and Baum & Wilczek (1988) § Linear error • Alystyne (1988) § Minkowski-r back-propagation • rth power of the absolute value of the error • Hanson & Burr (1988) • Alystyne (1988) • Weight adjustment procedure is derived by computing the change in the cost function with respect to the change in each weight • The derivation is extended so as to find the equation for adapting the connections between the FA and FB layers § each FB error is a proportionally weighted sum of the errors produced at the FC layer - 50 -
• The basic vanilla version back-propagation algorithm minimizes the squared error cost function and uses the three-layer elementary back propagation topology. Also known as the generalized delta rule. Advantages - it is capable of storing many more patterns than the number of FA dimensions - it is able to acquire complex nonlinear mappings Limitations - it requires extremely long training time - offline encoding - inability to know how to precisely generate any arbitrary mapping procedure The C++ class that handles the operations related to the ANN is presented in Listing 3. //----------------------------- 1: class nn 2: { 3: model&m; 4: int ni,nh,no; 5: float i[NI],h[NH],o[NO],eh[NH],eo[NO],w1[NI][NH],w2[NH][NO]; 6: public: 7: nn(model&,int,int,int); 8: void train(void); 9: void test(void); 10: void save(char*); 11: void load(char*); 12: private: 13: void randomWeights(void); 14: float f(float); 15: float df(float); 16: void pass(); 17: float trainSample(int); 18: }; //----------------------------- Listing 3. C++ class to handle the ANN The class presented offers the possibility to save a defined structure of ANN including the weights and to load an already developed one. The source of the ANN routines is presented in the appendix. - 51 -
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 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?