Recognition of facial expressions - Knowledge Based Systems ...

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- The discussions on the current approach and the performance achieved by testing the facial expression recognition models. All the processing steps are described in the current report. The final part contains the experiments run by using different functional approaches. - 14 -
LITERATURE SURVEY The recognition of facial expressions implies finding solutions to three distinct types of problems. The first one relates to detection of faces in the image. Once the face location is known, the second problem is the detection of the salient features within the facial areas. The final analysis consists in using any classification model and the extracted facial features for identifying the correct facial expression. For each of the processing steps described, there have been developed lots of methods to tackle the issues and specific requirements. Depending on the method used, the facial feature detection stage involves global or local analysis. The internal representation of the human face can be either 2D or 3D. In the case of global analysis, the connection with certain facial expressions is made through features determined by processing the entire face. The efficiency of methods as Artificial Neural Networks or Principal Component Analysis is greatly affected by head rotation and special procedures are needed to compensate the effects of that. On the other hand, local analysis performs encoding of some specific feature points and uses them for recognition. The method is actually used in the current paper. However, other approaches have been also performed at this layer. One method for the analysis is the internal representation of facial expressions based on collections of Action Units (AU) as defined in Facial Action Coding System (FACS) [Ekman W.V.Friesen, 1978] [Bartlett et al., 2004]. It is one of the most efficient and commonly used methodologies to handle facial expressions. The use of fiducial points on the face with the geometric positions and multi-scale and multi-orientation Gabor wavelet coefficients have been investigated by [Zhang, 1998; Zhang, 1999]. The papers describe the integration within an architecture based on a twolayer perceptron. According to the results reported by the author, the Gabor wavelet coefficients show much better results for facial expression recognition, when compared to geometric positions. In the paper [Fellenz et al., 1999] the authors compare the performance and the generalizing capabilities of several low-dimensional representations for facial expression
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 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 and 62: SMILE resides in a dynamic link lib
Page 63 and 64: 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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