Recognition of facial expressions - Knowledge Based Systems ...

More documents

Recommendations

Info

data taken from the Cohn-Kanade AU-Coded Facial Expression Database [Kanade et al. 2000]. Some processing was done so as to extract the useful information. More than that, since the original database contained only one image having the AU code set for each display, additional coding had to be done. The Bayesian network is used to encode the dependencies among the variables. The temporal dependencies were extracted to make the system be able to properly select the right emotional expression. In this way, the system is able to overcome the performance of the previous approaches that dealt only with prototypic facial expression [Pantic, Rothkrantz 2003]. The causal relationships track the changes occurred in each facial feature and store the information regarding the variability of the data. The typical problems of expression recognition have been tackled many times through distinct methods in the past. [Wang et al. 2003] proposed a combination of a Bayesian probabilistic model and Gabor filter. [Cohen et. all 2003] introduced a Tree- Augmented- Naive Bayes (TAN) classifier for learning the feature dependencies. A common approach was based on neural networks. [de Jongh, Rothkrantz 2004] used a neural network approach for developing an online Facial Expression Dictionary as a first step in the creation of an online Nonverbal Dictionary. [Bartlett et. all 2003] used a subset of Gabor filters selected with Adaboost and trained the Support Vector Machines on the outputs. The next section describes the literature survey in the field of facial expression recognition. Subsequently, different models for the current emotion analysis system will be presented in a separate section. The next section presents the experimental results of the developed system and the final section gives some discussions on the current work and proposes possible improvements. - 12 -
Project goal The current project aims at the realization of a uni-modal, fully automatic human emotion recognition system based on the analysis of facial expressions from still pictures. The use of different machine learning techniques is to be investigated for testing against the performance achieved in the current context of the project. The facial expression recognition system consists of a set of processing components that perform processing on the input video sequence. All the components are organized on distinct processing layers that interact for extracting the subtlety of human emotion and paralinguistic communication at different stages of the analysis. The main component of the system stands for a framework that handles the communication with the supported processing components. Each component is designed in such a way so as to comply with the communication rules of the framework through well defined interfaces. For experimental purposes, multiple components having the same processing goal can be managed in the same time and parallel processing is possible through a multithreading framework implementation. The following steps for building a facial expression recognition system are taken into consideration and detailed in the thesis: - A throughout literature survey that aims at describing the recent endeavors of the research community on the realization of facial expression recognition systems. - The presentation of the model, including the techniques, algorithms and adaptations made for an efficient determination of an automatic facial expression recognizer. - The implementation of the models described and the tools, programming languages and strategies used for integrating all the data processing components into a single automatic system. - The presentation of experimental setups for conducting a comprehensive series of tests on the algorithms detailed in the thesis. The presentation also includes the results of the tests that show the performance achieved by the models designed. - 13 -
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 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 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 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
Page 109 and 110:
- 109 -
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
show all

Recognition of facial expressions - Knowledge Based Systems ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?