Abstract
In practical applications of pattern recognition and computer vision, the performance of many approaches can be improved by using multiple models. In this paper, we develop a common theoretical framework for multiple model fusion at the feature level using multilinear subspace analysis (also known as tensor algebra). One disadvantage of the multilinear approach is that it is hard to obtain enough training observations for tensor decomposition algorithms. To overcome this difficulty, we adopted the M2SA algorithm to reconstruct the missing entries of the incomplete training tensor. Furthermore, we apply the proposed framework to the problem of face image analysis using Active Appearance Model (AAM) to validate its performance. Evaluations of AAM using the proposed framework are conducted on Multi-PIE face database with promising results.
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References
Cootes, T., Wheeler, G., Walker, K., Taylor, C.: View-based active appearance models. Image and Vision Computing 20(9), 657–664 (2002)
Zhu, X., Ramanan, D.: Face detection, pose estimation, and landmark localization in the wild. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2879–2886. IEEE (2012)
Gonzalez-Mora, J., De la Torre, F., Murthi, R., Guil, N., Zapata, E.L.: Bilinear active appearance models. In: Proc. IEEE 11th Int. Conf. Computer Vision, ICCV 2007, pp. 1–8 (2007)
Lee, J., Moghaddam, B., Pfister, H., Machiraju, R.: A bilinear illumination model for robust face recognition. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 2, pp. 1177–1184. IEEE (2005)
Vasilescu, M., Terzopoulos, D.: Multilinear subspace analysis of image ensembles. In: Proceedings of Computer Vision and Pattern Recognition, vol. 2, p. II-93. IEEE (2003)
Lee, H.S., Kim, D.: Tensor-based AAM with continuous variation estimation: Application to variation-robust face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(6), 1102–1116 (2009)
Gross, R., Matthews, I., Baker, S.: Generic vs. person specific active appearance models. Image and Vision Computing 23(12), 1080–1093 (2005)
De Lathauwer, L., De Moor, B., Vandewalle, J.: A multilinear singular value decomposition. SIAM Journal on Matrix Analysis and Applications 21(4), 1253–1278 (2000)
Geng, X., Smith-Miles, K., Zhou, Z., Wang, L.: Face image modeling by multilinear subspace analysis with missing values. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(3), 881–892 (2011)
Cootes, T., Edwards, G., Taylor, C.: Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)
Gross, R., Matthews, I., Cohn, J., Kanade, T., Baker, S.: Multi-PIE. Image and Vision Computing 28(5), 807–813 (2010)
Kolda, T., Bader, B.: Tensor decompositions and applications. SIAM Review 51(3), 455–500 (2009)
Acar, E., Dunlavy, D., Kolda, T., Mørup, M.: Scalable tensor factorizations for incomplete data. Chemometrics and Intelligent Laboratory Systems 106(1), 41–56 (2011)
Cootes, T., Taylor, C., Cooper, D., Graham, J., et al.: Active shape models-their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Cristinacce, D., Cootes, T.: Feature detection and tracking with constrained local models. In: Proc. British Machine Vision Conference, vol. 3, pp. 929–938 (2006)
Matthews, I., Baker, S.: Active appearance models revisited. International Journal of Computer Vision 60(2), 135–164 (2004)
Feng, Z.H., Kittler, J., Christmas, W., Wu, X.J., Pfeiffer, S.: Automatic face annotation by multilinear AAM with missing values. In: Proc. 21st International Conference on Pattern Recognition, ICPR (2012)
Edwards, G., Cootes, T., Taylor, C.: Advances in active appearance models. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 1, pp. 137–142 (1999)
Bader, B.W., Kolda, T.G., et al.: Matlab tensor toolbox version 2.5 (January 2012)
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Feng, ZH., Kittler, J., Christmas, W., Wu, XJ. (2013). Feature Level Multiple Model Fusion Using Multilinear Subspace Analysis with Incomplete Training Set and Its Application to Face Image Analysis. In: Zhou, ZH., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2013. Lecture Notes in Computer Science, vol 7872. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38067-9_7
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DOI: https://doi.org/10.1007/978-3-642-38067-9_7
Publisher Name: Springer, Berlin, Heidelberg
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