Feature extraction of facial action units combining kernel methods and independent component analysis
Keywords:facial expression recognition, action unit, independent component analysis, kernel methods
The work described in this paper presents a methodology for characterizing facial action units (AUs), which represents the subtle change of facial expressions, based on Kernels Methods perform a nonlinear maping of data and looking for directions to the projections of the data in feature space through independent component analysis (ICA). The methodology validation was done on Cohn-Kande database. Image preprocessing was done through histogram equalization, a whitening on data with Kernel Principal Component Analysis (KPCA), for that the mapped in feature space search a lineal structure of the input data, finally we applied ICA for make the projected distribution of data is at least possible Gaussian. The results were 96.64% accurate for average recognition of three combinations of facial AUs of the whole face more neutral faces. Mainly changes that occur between rapid transitions of AUs instantly shown were detected. Additionally the proposed methodology can reduce the size of the feature space because data only in terms of independent components, are presented so as to use only those variables that provided greater information, which reduces the complexity of the classifier.
Y. Tian, T. Kanade, J. Cohn. “Recognizing Action Units for Facial Expression Analysis”. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 23. 2001. pp. 97-115.
T. Kanade, J. Cohn, Y. Tian. “Comprehensive database for facial expression analysis”. Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition. Grenoble (France). 2000. pp. 46-53.
M. S. Bartlett. Face image analysis by unsupervised learning and redundancy reduction. Ph.D. dissertation. Univ. California. San Diego. 1998. pp. 27-92.
G. Donato, M. S. Bartlett, J. C. Hager, P. Ekman, T. J. Sejnowski. “Classifying facial actions”. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 21. 1999. pp. 974-989.
A. R. Webb. Statistical Pattern Recognition. 2a ed. Ed. John Wiley & Sons. Indianapolis. USA. 2002. pp. 305- 318.
R. O. Duda, M. E. Hart, D. G. Stork. Pattern Classification. 2a ed. Ed. Wiley Interscience. Hoboken. USA. 2000. pp. 20-83.
B. Schölkopf, A. J. Smola. Learning with Kernels. MA. Ed. MIT Press. Cambridge. 2002. pp. 25-55.
T. Martiriggiano, M. Leo, P. Spagnolo, Dapos, T. Orazio. “Facial feature extraction by kernel independent component analysis”. AVBPS Advanced Video and Signal Based Surveillance. IEEE Computer Society. Los Alamitos (CA). 2005. pp. 270-275.
T. Martiriggiano, M. Leo, T. D’Orazio, A. Distante. “Face Recognition by Kernel Independent Component Analysis”. IEA/AIE’2005: Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence. Ed. Springer-Verlag. London. 2005. pp. 55-58.
X. Yang, X. Gao, D. Zhang, J. Yang. “Kernel ica: An alternative formulation and its application to face recognition”. Pattern Recognition. Vol. 38. 2005. pp. 1784-1787.
J. J. Lien, T. Kanade, A. Zlochower, J. F. Cohn, C.-C. Li. “Automatically recognizing facial expressions in the spatio-temporal domain”. Workshop on Perceptual User Interfaces. Vol. 36. 1997. pp. 94-7.
J. J. Lien, T. Kanade, J. F. Cohn, C.C. Li. “Automated facial expression recognition based on facs action units”. FG ‘98: Proceedings of the 3rd. International Conference on Face & Gesture Recognition. Nara (Japan). Vol. 14. 1998. pp. 390-395.
M. S. Bartlett, J. C. Hager, P. Ekman, T. J. Sejnowski. “Measuring facial expressions by computer image analysis”. Research supported by NSF Grant No BS- 9120868, Lawrence Livermore National Laboratories Intra–University Agreement B291436 and Howard Hughes Medical Institute. San Diego (CA). 1999. pp. 1-25.
M. S. Bartlett, G. Littlewort, C. Lainscsek, I. Fasel, J. Movellan. Machine learning methods for fully automatic recognition of facial expressions and facial actions. Proc. IEEE Int’l Conf. Systems, Man and Cybernetics. Hague (Netherlands). 2004. pp. 592-597.
J. Bazzo, M. Lamar. “Recognizing facial actions using gabor wavelets with neutral face average diference”. Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition. Seoul. Korea. 2004. pp. 505-510.
M. Valstar, M. Pantic, I. Patras. “Motion history for facial action detection in video”. IEEE International Conference on Systems, Man and Cybernetics. Delft (Netherlands). 2004. pp. 635-640.
M. Valstar, I. Patras, M. Pantic. “Facial Action Unit Recognition using Temporal Templates”. IEEE Int’l Workshop on Human-Robot Interaction. Kurashiki Okayama (Japan). 2004. pp. 253-258.
M. Valstar, M. Pantic. “Fully automatic facial action unit detection and temporal analysis”. IEEE Int’l Conf. on Computer Vision and Pattern Recognition. New York. Vol. 3. 2006. pp.18.
C. F. Chuang, F. Y. Shih. “Rapid and brief communication: Recognizing facial action units using independent component analysis and support vector machine”. Pattern Recognition. Vol. 39. 2006. pp. 1795-1798.
C. Campbell. “Kernel methods: a survey of current techniques”. Neurocomputing. Vol. 48. 2000. pp. 63- 84. 21. A. J. Bell, T. J. Sejnowski. “An information maximization approach to blind separation and blind deconvolution”. Neural Computation. Vol. 7. 1995. pp. 1129-1159.
A. Hyvärinen. “Fast and robust fixed-point algorithms for independent component analysis”. IEEE Transactions on Neural Networks. Vol. 10. 1999. pp. 626-634.
M. A. Vicente, P. O. Hoyer, A. Hyvarinen. “Equivalence of some common linear feature extraction techniques for appearance-based object recognition tasks”. IEEE Trans. Pattern Anal. Mach. Intell. Vol. 29. 2007. pp. 896-900.
Schölkopf, A. Smola, K.R. Müller. “Nonlinear component analysis as a Kernel eigenvalue problem”. Neural Computation. Vol. 10. 1998. pp. 1299-1319.
M. L. Guevara, J. D. Echeverry, W. A. Urueña. “Detección de rostros en imágenes digitales usando clasificadores en cascada”. Scientia et Technica. Vol. 38. 2008. pp. 1-6.
Q. Gao, L. Zhang, D. Zhang. “Sequential row–column independent component analysis for face recognition”. Neurocomputing. Vol. 72. 2009. pp. 1152-1159.
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