Recognition of facial expressions - Knowledge Based Systems ...

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of prototypic emotions (i.e., joy, surprise, anger, fear, disgust, and sadness). For the available portion of the database, these six tasks and mouth opening in the absence of other action units were coded by a certified FACS coder. Seventeen percent of the data were comparison coded by a second certified FACS coder. Inter-observer agreement was quantified with coefficient kappa, which is the proportion of agreement above what would be expected to occur by chance (Cohen, 1960; Fleiss, 1981). The mean kappa for inter-observer agreement was 0.86. Image sequences from neutral to target display were digitized into 640 by 480 or 490 pixel arrays with 8-bit precision for grayscale values. The image format is “png”. Images were labeled using their corresponding VITC. FACS codes for the final frame in each image sequence were available for the analysis. In some cases the codes have been revised. The final frame of each image sequence was coded using FACS action units (AU), which are reliable descriptions of the subject's expression. In order to make the task of computing the model parameter values possible, a software application was developed. It offered the possibility to manually plot certain points on each image of the database in an easy manner. The other components of the system automatically computed the values of the parameters so as to be ready for the training step for the neuronal networks or for computing the probabilities table in the case of BBN. SMILE BBN library SMILE [Structural Modeling, Inference, and Learning Engine] is a fully platform independent library of C++ classes implementing graphical probabilistic and decision theoretic models, such as Bayesian networks, influence diagrams, and structural equation models. It was designed in a platform independent fashion as an object oriented robust platform. It has releases starting from 1997. The interface is so defined as to provide the developers with different tools for creating, editing, saving and loading of graphical models. The most important feature is related to the ability to use the already defined models for probabilistic reasoning and decision making under uncertainty. The release of - 60 -
SMILE resides in a dynamic link library. It can be embedded in programs that use graphical probabilistic models as their engines for reasoning. Individual classes of SMILE are accessible from C++ or (as functions) from C programming languages. There also exists an ACTIVEX component as an alternative for embedding the library in the program that is supposed to have access to the SMILE routines. That makes possible for different programs that have been developed under distinct programming languages to still be able to query SMILE functionality. Primary features • It is platform independent. There are versions available for Unix/Solaris, Linux and PC • The SMILE.NET module is available for use with .NET framework. It is compatible with all .NET languages including C# and VB.NET. It may be used for developing web-based applications of Bayesian networks • It includes a very thorough and complete documentation GeNIe BBN Toolkit The GeNIe stands from Graphical Network Interface and is a software package that can be used to intuitively create decision theoretic models using a graphical click-and-drop interface. In addition to the capability to graphically design BBN models, it offers the possibility for testing and performing reasoning. The feature is realized by the integration of SMILE library. The latest version of GeNIe is GeNIe 2.0. It came as an improvement for the previous version, GeNIe 1.0 (1998) and includes new algorithms and techniques based on the various suggestions and requirements from the users. The great advantage of using GeNIe is that the models can be quickly developed and tested by using an easy graphic interface. Once a model is ready, it can be integrated in a program as support for a backend engine by using SMILE functionality. - 61 -
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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 ..............
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 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: IMPLEMENTATION Facial Feature Datab
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
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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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