Recognition of facial expressions - Knowledge Based Systems ...

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483: 138 004 5+25+27 3 3 3 6 4 2 2 6 6 2 484: 138 005 6+7+12+25 3 3 2 2 1 1 2 1 2 6 485: 138 006 6+7+12Y 3 3 2 4 1 1 2 1 2 6 //---------------------------------------------------------------------------------------------- Listing 4. The parameter discretization result The more classes for the discretization process are, the higher the final emotion recognition rate was achieved. It was also found that after a certain value, the additional recognition percent obtained as result of increasing the number of the classes decreases. The discretization process on parameters was mainly implied by the presence of Bayesian Belief Network reasoning. There was no need for discretization of parameters in the case only neural network computations are run. In that case, for instance, it would have determined a certain number of bits any value of the parameters could be represented and the values could directly be encoded on the neurons in the input layer. Another option would have been to work directly with values of the neurons in a given interval. Any value taken by the parameters could be scaled to the correspondent in the interval and encoded in the proper input neuron. In the case of Bayesian Belief Networks, the number of classes determines the number of states of each parameter. In the testing stage of the system, a new set of Facial Characteristic Points is provided by the FCP detection module. Based on the values, a set of parameter values is obtained following the computations. In case there is any value exceeding the limits of the interval, for one of the parameters, the number according to the nearest class is used instead. The BBN based reasoning is done by setting the class values of the parameters as evidence in the network and by computing the anterior probabilities of the parameters. Finally, the probabilities according to each emotional class are read from the proper parameter. Facial Expression Assignment Application Once the FCPs were specified in the set of 485 images and the 10 parameter values were computed, a software application processed each sequence of FACS for assigning the correct emotion to each face. - 72 -
The functionality of the tool was based on a set of translation rules for re-labeling the AU coding into emotion prototypes as defined in the Investigator's Guide to the FACS manual, FACS 2002, Ekman, Friesen & Hager (Table 8). A C++ program was written to process a text input file and to translate each FACS sequence of each sample into the correspondent facial emotion. Table 8. Emotion predictions As output, the application provided also the field associated with the facial expression in addition to the FACS field for each image sample. The information in the Table 8 was loaded from a text file saved on the disk. The content of the file is according to Listing 5. //--------------------------------------------------------------- 1: Surprise 2: :prototypes 3: 1+2+5b+26 4: 1+2+5b+27 5: :variants 6: 1+2+5b 7: 1+2+26 - 73 -
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Automatic recognition of facial exp
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Man-Machine Interaction Group Facul
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Acknowledgements The author would l
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Eye Detection Module ..............
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List of tables Table 1. The used se
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data taken from the Cohn-Kanade AU-
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- The discussions on the current ap
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ecognition in static pictures, for
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In [Wang and Tang, 2003] the author
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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 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: Figure 17. The facial areas involve
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
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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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