Recognition of facial expressions - Knowledge Based Systems ...

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T The term rank( X ∗ X ) is generally equal to K and a reduction of dimension has been made. A further reduction of dimension can be made. At the testing session, an image representing a given type of facial expression is taken. A reconstruction procedure is done for determining which emotional class the image can be associated with. The mechanism is based actually on determining the facial area image that is closer to the new image in the set. The emotional class is that of the image for whom the error is minimum. PCA mechanism has been also used as direct classifier for the facial emotions. The initial set of data consisted of 10 parameter values for each sample from the database. Each sample has been represented by a label having the description of the facial expression associated to the sample. Because the data included the values of the parameters and not all the pixels in the image space, the PCA methodology was not used for reducing the dimensionality of the data. Instead the result reflected the rotation on the axes so as to have high efficiency in projecting the input vectors in the axes for a correct classification of facial expression. The results of the experiment using PCA as direct classifier can be seen in Experiments section. - 44 -
Artificial Neural Networks Back-Propagation The ANN represents learning mechanisms that are inspired from the real world. The structure of such a mathematical abstraction would consist in a set of neurons presenting a certain type of organization and the specific neuronal interconnections. The Back Propagation approach for the ANN implies that the learning process takes place on the base of having learning samples for input and output patterns. In the case of learning such a system to model the mechanism of classifying facial expressions, it is required to have a set of input and output sample data. There would be two stages, one for making the system aware of the structure and associations of the data, so that is the training stage. The second step would be that of testing. In the training step, the system would build the internal knowledge, based on the presented patterns to be learned. The knowledge of the system resides in the weights associated to the connections between the neurons. The training of the ANN is done by presenting the network with the configuration of the input parameters and the output index of facial expression. Both kinds of data are encoded as values in the network neurons. The input parameters are encoded on the neurons grouped in the input layer of the network. In the same way, the emotion index in encoded in the neuron(s) in the output layer. For the experiments run on the current project, a back-propagation neural network was used as the classifier for the facial expressions. The topology of the network describes three neuron layers. The input layer is set to handle the input data according to the type of each experiment. The data refer to the parameters of the model used for analysis. Encoding the parameters in the neurons There are two type of encoding a value in the neurons of the ANN. Since the neurons present values in the ANN, each neuron can be used for storing almost any kind of - 45 -
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 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
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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
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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