Recognition of facial expressions - Knowledge Based Systems ...

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Abstract Automatic recognition of facial expressions Dragos Datcu, student number: 1138758 Delft, November 2004 Man-Machine Interaction Group Faculty of Electrical Engineering, Mathematics, and Computer Science Delft University of Technology Mekelweg 4, 2628 CD Delft, The Netherlands Members of the Supervising Committee drs. dr. L.J.M. Rothkrantz prof. dr. H. Koppelaar prof. dr. ir. E.J.H. Kerckhoffs ir. F. Ververs The study of human facial expressions is one of the most challenging domains in pattern research community. Each facial expression is generated by non-rigid object deformations and these deformations are person dependent. The goal of this MSc. project is to design and implement a system for automatic recognition of human facial expression in video streams. The results of the project are of a great importance for a broad area of applications that relate to both research and applied topics. As possible approaches on those topics, the following may be mentioned: automatic surveillance systems, the classification and retrieval of image and video databases, customer-friendly interfaces, smart environment human computer interaction and research in the field of computer assisted human emotion analyses. Some interesting implementations in the field of computed assisted emotion analysis concern experimental and interdisciplinary psychiatry. Automatic recognition of facial expressions is a process primarily based on analysis of permanent and transient features of the face, which can be only assessed with errors of some degree. The expression recognition model is oriented on the specification of Facial Action Coding System (FACS) of Ekman and Friesen. The hard constraints on the scene processing and recording conditions set a limited robustness to the analysis. A probabilistic oriented framework is used in order to manage the uncertainties and lack of information. The support for the specific processing involved was given through a multimodal data fusion platform. The Bayesian network is used to encode the dependencies among the variables. The temporal dependencies are to be extracted to make the system be able to properly select the right expression of emotion. In this way, the system is able to overcome the performance of the previous approaches that dealt only with prototypic facial expression. IV
Acknowledgements The author would like to express his gratitude to his supervisor, Professor Drs. Dr. Leon Rothkrantz for facilitating the integration in the research field, for the trust and for all his support in making the project work come to an end. Additionally, he thanks all his friends he met during his stay in Delft for all the constructive ideas, excellent advices and nice moments spent together. Special appreciation goes to the community of Romanian students at TUDelft. Dragos Datcu September, 2004 V
Page 1 and 2: Automatic recognition of facial exp
Page 3: Man-Machine Interaction Group Facul
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 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
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way high pass filter were used for
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module includes some routines for d
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IMPLEMENTATION Facial Feature Datab
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SMILE resides in a dynamic link lib
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FCP Management Application The Cohn
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Figure 13. Head rotation in the ima
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Table 6. The set of rules for the u
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[image width] [image height] ---- A
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Figure 17. The facial areas involve
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The functionality of the tool was b
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A small part of the output text fil
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o call a specialized routine for co
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There is another kind of structure
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performing classification of facial
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Figure 22. Sobel edge detector appl
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almost closed it obviously does not
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Figure 28. FCP detection The effici
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TESTING AND RESULTS The following s
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BBN experiment 2 “Detection of fa
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General recognition rate is 63.77%
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Recognition results. Confusion Matr
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5 states model General recognition
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LVQ experiment “LVQ based facial
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ANN experiment Back Propagation Neu
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PCA experiment “Principal Compone
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Eigenvalues: Eigenvectors: Factor l
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Squared cosines of the variables: C
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- 109 -
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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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