Abstract
In this paper, we propose a new scheme of Gabor-based face recognition. Based on the fact that different Gabor filters have different properties, we first learn discriminating subspace for each kind of Gabor images respectively. Then the boosting learning is performed to fuse all the Gabor discriminating subspaces for recognition. Compared with previous work, the proposed method has three contributions: (1). We make sufficiently use of the respective properties of the Gabor filters, and learn different discriminant subspaces for different Gabor images respectively; (2). Boosting based fusing method adaptively determines the discriminating vectors and dimensionality of each subspace according to its discriminating capacity, so as to further improve the recognition performance; (3). The problem of computational complexity is well handled by subspace analysis and boosting based fusion. Extensive experiments show its encouraging performance.
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References
Zhao, W., Chellappa, R., Phillips, A.P.J.: Face recognition: A literature survey. CS-Tech Report-4167, University of Maryland (2000)
Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 72–86 (1991)
Zhao, W., Chellappa, R.: Subspace linear discriminant analysis for face recognition. Tech Report CAR-TR-914, Center for Automation Research, University of Maryland (1999)
Liu, C.J., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. on Image Processing 11(4), 467–476 (2002)
Lades, M., Vorbrggen, J.C., Buhman, J., Lange, J., von der Malburg, C., Wortz, R.: Distortion invariant object recognition in the dynamic link architecture. IEEE Trans on Computers 42, 300–311 (1993)
Yang, P., Shan, S.G., Li., W.S., Zhang, D.: Face recognition using ada-boosted gabor features. In: Proc. of Int’l Conf. on Automatic Face and Gesture Recognition (2004)
Moghaddam, B., Jebara, T., Pentland, A.: Bayesian face recognition. Pattern Recognition 33, 1771–1782 (2000)
Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The feret database and evaluation procedure for face recognition algorithms. Image and Vision Computing 16, 295–306 (1998)
Gao, W., Cao, B., Shan, S.G.: The cas-peal large-scale face database and evaluation protocols. Tech-Report JDL-TR-04-FR-001, JDL, CAS (2004)
Tang, X., Li, Z.F.: Frame synchronization and multi-level subspace analysis for video based face recognition. In: Proc. of Int’l Conf. Computer Vision and Pattern Recognition (2004)
Burr, D., Morrone, M., Spinelli, D.: Evidence for edge and bar detectors in human vision. Vision Research 29, 419–431 (1989)
Wang, X., Tang, X.: Unified subspace analysis for face recognition. In: Proc. of Int’t Conf. Computer Vision (2003)
Freund, Y., Schapire, R.E.: A decision theoretic generalization of online learning and an application to boosting. J. Comp. ans Sys. Sci. 55, 119–139 (1997)
Tieu, K., Viola, P.: Boosting image retrieval. In: Proc. of Int’l Conf. Computer Vision and Pattern Recognition (2000)
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© 2006 Springer-Verlag Berlin Heidelberg
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Liu, Q., Jin, H., Tang, X., Lu, H., Ma, S. (2006). Boosting Multi-gabor Subspaces for Face Recognition. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_55
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DOI: https://doi.org/10.1007/11612032_55
Publisher Name: Springer, Berlin, Heidelberg
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