Recognition of facial expressions - Knowledge Based Systems ...

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Lien, J., T. Kanade, J. Cohn, C. C. Li, ‘Detection, tracking and classification of action units in facial expression’, Journal of Robotics and Autonomous Systems, 31:131–146, 2000 Lyons, M. J., J. Budynek, S. Akamatsu, ‘Automatic classification of single facial images’, IEEE Trans. Pattern Anal. Machine Intell., vol. 21, no. 12, pp. 1357–1362, 1999 Morimoto, C., D. Koons, A. Amir, M. Flickner, ‘Pupil detection and tracking using multiple light sources’, Technical report, IBM Almaden Research Center, 1998 Moriyama, T., J. Xiao, J. F. Cohn, T. Kanade, ‘Meticulously detailed eye model and its application to analysis of facial image’, Proceedings of IEEE SMC, pp.580–585, 2004 Padgett, C., G. Cottrell, “Representing face images for emotion classification”, In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems, vol. 9, Cambridge, MA, MIT Press, 1997 Padgett, C., G. Cottrell, R. Adolphs, “Categorical perception in facial emotion classification”, In Proceedings of the 18th Annual Conference of the Cognitive Science Society, Hillsdale NJ, Lawerence Erlbaum. 5, 1996 Pantic, M., L. J. M. Rothkrantz, ‘Toward an Affect-Sensitive Multimodal Human- Computer Interaction’ IEEE proceedings vol. 91, no. 9, pp. 1370-1390, 2003 Pantic, M., L. J. M. Rothkrantz, ‘Self-adaptive expert system for facial expression analysis’, IEEE International Conference on Systems, Man and Cybernetics (SMC ’00), pp. 73–79, October 2000 Pantic, M., L .J. M. Rothkrantz, ‘Automatic analysis of facial expressions: the state of the art’, IEEE Trans. PAMI, 22(12), 2000 Pantic, M., L. J. M. Rothkrantz, ‘An expert system for multiple emotional classification of facial expressions’, Proceedings, 11th IEEE International Conference on Tools with Artificial Intelligence, pp. 113-120, 1999 Phillips, P., H. Moon, P. Rauss, S. Rizvi, ‘The FERET september 1996 database and evaluation procedure’ In Proc. First Int’l Conf. on Audio and Video-based Biometric Person Authentication, pages 12–14, Switzerland, 1997 Rosenblum, M., Y. Yacoob, L. S. Davis, ‘Human expression recognition from motion using a radial basis function network architecture’, IEEE Trans. NNs, vol. 7, no. 5, pp. 1121–1137, 1996 Salovey, P., J. D. Mayer, ‘Emotional intelligence’ Imagination, Cognition, and Personality, 9(3): 185-211, 1990 - 116 -
Samal, A., P. Iyengar, ‘Automatic recognition and analysis of human faces and facial expression’, A survey. Pattern Recognition, 25(1):65–77, 1992 Schweiger, R., P. Bayerl, H. Neumann, “Neural Architecture for Temporal Emotion Classification”, ADS 2004, LNAI 2068, Springer-Verlag Berlin Heidelberg, pp. 49-52, 2004 Seeger, M., ‘Learning with labeled and unlabeled data’, Technical report, Edinburgh University, 2001 Stathopoulou, I. O., G. A. Tsihrintzis, ‘An improved neuralnetwork-based face detection and facial expression classification system’, Proceedings of IEEE SMC, pp. 666–671, 2004 Tian, Y., T. Kanade, J. F. Cohn. ‘Recognizing upper face action units for facial expression analysis’ In Proceedings of Conference on Computer Vision and Pattern Recognition, June 2000 Tian, Y., T. Kanade, J. F. Cohn. ‘Recognizing action units for facial expression analysis’ Pattern Analysis and Machin Intelligence, 23(2), February 2001 Turk, M., A. Pentland, ‘Face recognition using eigenfaces”, Proc. CVPR, pp. 586-591, 1991 Yacoob Y., L. Davis. ‘Computing spatio-temporal representation of human faces’ In CVPR, pages 70–75, Seattle, WA, June 1994 Yin, L., J.Loi, W.Xiong, ‘Facial expression analysis based on enhanced texture and topographical structure’, Proceedings of IEEE SMC, pp. 586–591, 2004 Zhang, Z., ‘Feature-based facial expression recognition: Sensitivity analysis and experiments with a multilayer perceptron’ International Journal of Pattern Recognition and Artificial Intelligence, 13(6):893–911, 1999 Zhang, Z., M. Lyons. M. Schuster, S. Akamatsu, ‘Comparison between geometry based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron’, in Proc. IEEE 3rd Int’l Conf. on Automatic Face and Gesture Recognition, Nara, Japan, April 1998 Zhu, Z., Q. Ji, K. Fujimura, K. Lee, ‘Combining Kalman filtering and mean shift for real time eye tracking under active IR illumination,’ in Proc. Int’l Conf. Pattern Recognition, Aug. 2002 Wang, X., X. Tang, ‘Bayesian Face Recognition Using Gabor Features’, Proceedings of the 2003 ACM SIGMM Berkley, California 2003 - 117 -
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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
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data taken from the Cohn-Kanade AU-
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- The discussions on the current ap
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ecognition in static pictures, for
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In [Wang and Tang, 2003] the author
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Data preparation Starting from the
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Figure 2. Facial characteristic poi
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The only additional time is that of
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African-American and three percent
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Table 4. The emotion projections of
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contains a large sample of varying
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Bayesian networks were designed to
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- correctly identify the goals of m
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In the final step of constructing a
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- renormalize the w ijk to assure t
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Principal Component Analysis The ne
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The term σ ij is the covariance be
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T The term rank( X ∗ X ) is gener
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numeric information. Usually, a neu
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defined as: ∆w = −η ∇ ji ji
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∂E ∂a j = i E ∂a pk ∂a pk
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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
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: Essa, A. Pentland, ‘Coding, analy
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
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