Recognition of facial expressions - Knowledge Based Systems ...

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is directly associated to the expression of the input face (Figure 29). Given the parameters from the expression recognition module, the system computes the shape of different visual features and generates a 2D graphical face model. Figure 29. The response of the system The geometrical shape of each visual feature follows certain rules that aim to set the outlook to convey the appropriate emotional meaning. Each feature is reconstructed using circles and simple polynomial functions as lines, parabola parts and cubic functions. A five-pixel window is used to smooth peaks so as to provide shapes with a more realistic appearance. The eye upper and lower lid was approximated with the same cubic function. The eyebrow’s thickness above and below the middle line was calculated from three segments as a parabola, a straight line and a quarter of a circle as the inner corner. A thickness function was added and subtracted to and from the middle line of the eyebrow. The shape of the mouth varies strongly as emotion changes from sadness to happiness or disgust. The manipulation of the face for setting a certain expression implies to mix different emotions. Each emotion has a percentage value by which they contribute to the face general expression. The new control set values for the visual features are computed by the difference of each emotion control set and the neutral face control set, and make a linear combination of the resulting six vectors. - 88 -
TESTING AND RESULTS The following steps have been taken into account for training the models for the facial expression recognition system: - Obtaining the (Cohn-Kanade) database for building the system’s knowledge - Conversion of data base images from ‘png’ to ‘bmp’ format, 24 bits/pixel - Increasing the quality of the images through some enhancement procedures (light, removing strips, applying filters, etc.) - Extracting some Facial Characteristic Points (FCPs) by using a special tool (FCP Management Application) - Computing the value of some parameters according to a given model. Applying a discretization procedure by using a special application (Parameter Discretization Application) - Determining the facial expression for each of the samples in the database by analyzing the sequence of Action Units (AUs). The tool used to process the files in the database was Facial Expression Assignement Application. - Using different kind of reasoning mechanisms for emotion recognition. The training step took into account the data provided from the previous steps. Bayesian Belief Networks (BBN) and back-propagation Artificial Neuronal Networks (ANN) were the main modalities for recognition. - Principal Component Analysis technique was used as an enhancement procedure for the emotion recognition. The steps that imply testing the recognition models are: - Capture the video signal containing the facial expression - Detecting the Facial Characteristic Points automatically - Computing the value of the model parameters - Using the parameter values for emotion detection
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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
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Figure 2. Facial characteristic poi
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The only additional time is that of
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African-American and three percent
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Table 4. The emotion projections of
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contains a large sample of varying
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Bayesian networks were designed to
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- correctly identify the goals of m
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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 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: Figure 28. FCP detection The effici
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
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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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