Recognition of facial expressions - Knowledge Based Systems ...

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Primary Features • Cross compatibility with other software through the support for other file types (Hugin, Netica, Ergo) • Support for handling observation costs of nodes • Support for diagnostic case management • Supports chance nodes with General, Noisy OR/MAX and Noisy AND distribution GeNIe Learning Wizard component The module is part of GeNIe Toolkit version 1.0. It can be used for performing automatic discovery of causal models from data. It includes several learning algorithms, including constraint-based and Bayesian methods for learning structure and parameters. In addition to this it offers support for discrete, continuous and mixed data. The missing data are handled through a variety of special methods. There are several simple and advanced methods included for discretization of data. The user can specify many forms of background knowledge. - 62 -
FCP Management Application The Cohn-Kanade AU-Coded Facial Expression Database was used to create the initial knowledge for the system reasoning mechanism. The database consists of a series of image sequences done by 100 university students. The sequences contain facial expressions, according to the specifications given by the experimenter to the students. There are both single and combinations of Action Units. The most important part of the database stands for the set of facial images that were coded by means of Action Units. There are 485 distinct images and each has the correspondent AU sequence. In order to be useful for the emotion recognition system, the AU coded images has to be analyzed for extracting the location of some given Facial Characteristic Points (FCPs). A preprocessing step has been involved for preparing each image for being ready for processing. There has been applied some simple procedures to increase the quality of the images. Each of the points has an exact position on the surface of the face. For making the process of the point extraction easier, a software application has been developed. The application has a friendly interface and offers the possibility to manually set the full set of 36 FCPs in a graphical manner. Initially, the image is loaded by choosing an option in the menu or by clicking on a given button in the application’s toolbar ( ). The image has to be stored in a BMP format with 24 bits per pixel. Other image formats are not allowed. As soon as the image is loaded in the memory, it is shown on the surface of the application. From the current point, the user is assumed to specify the location of each of the 36 FCPs by clicking with the mouse on the certain image location. The first two points are used to specify the FCP of the inner corner of the eyes. The information is further taken for computing the degree of the rotation of the head. The angle is computed by using formula in equation 20. - 63 -
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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
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 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: SMILE resides in a dynamic link lib
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
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Page 111 and 112: CONCLUSION The human face has attra
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REFERENCES Almageed, W. A., M. S. F
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Essa, A. Pentland, ‘Coding, analy
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Samal, A., P. Iyengar, ‘Automatic
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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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