×

Incremental nonnegative matrix factorization for face recognition. (English) Zbl 1162.94306

Summary: Nonnegative matrix factorization (NMF) is a promising approach for local feature extraction in face recognition tasks. However, there are two major drawbacks in almost all existing NMF-based methods. One shortcoming is that the computational cost is expensive for large matrix decomposition. The other is that it must conduct repetitive learning, when the training samples or classes are updated. To overcome these two limitations, this paper proposes a novel incremental nonnegative matrix factorization (INMF) for face representation and recognition. The proposed INMF approach is based on a novel constraint criterion and our previous block strategy. It thus has some good properties, such as low computational complexity, sparse coefficient matrix. Also, the coefficient column vectors between different classes are orthogonal. In particular, it can be applied to incremental learning. Two face databases, namely FERET and CMU PIE face databases, are selected for evaluation. Compared with PCA and some state-of-the-art NMF-based methods, our INMF approach gives the best performance.

MSC:

94A08 Image processing (compression, reconstruction, etc.) in information and communication theory
68T10 Pattern recognition, speech recognition

Software:

FERET; CMU PIE
PDFBibTeX XMLCite
Full Text: DOI EuDML

References:

[1] R. Chellappa, C. L. Wilson, and S. Sirohey, “Human and machine recognition of faces: a survey,” Proceedings of the IEEE, vol. 83, no. 5, pp. 705-741, 1995. · doi:10.1109/5.381842
[2] W. Zhao, R. Chellappa, A. Rosenfeld, and J. Phillips, “Face recognition: a literature survey,” Tech. Rep. CFAR-TR00-948, University of Maryland, College Park, Md, USA, 2000.
[3] R. Brunelli and T. Poggio, “Face recognition: features versus templates,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp. 1042-1052, 1993. · Zbl 05112530 · doi:10.1109/34.254061
[4] D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization,” Nature, vol. 401, no. 6755, pp. 788-791, 1999. · Zbl 1369.68285 · doi:10.1038/44565
[5] D. D. Lee and H. S. Seung, “Algorithms for non-negative matrix factorization,” in Proceedings of the Advances in Neural Information Processing Systems (NIPS ’01), vol. 13, pp. 556-562, Vancouver, Canada, December 2001.
[6] M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991. · doi:10.1162/jocn.1991.3.1.71
[7] S. Z. Li, X. W. Hou, H. J. Zhang, and Q. S. Cheng, “Learning spatially localized, parts-based representation,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR ’01), vol. 1, pp. 207-212, Kauai, Hawaii, USA, December 2001. · doi:10.1109/CVPR.2001.990477
[8] S. Wild, J. Curry, and A. Dougherty, “Improving non-negative matrix factorizations through structured initialization,” Pattern Recognition, vol. 37, no. 11, pp. 2217-2232, 2004. · Zbl 02116611 · doi:10.1016/j.patcog.2004.02.013
[9] I. Buciu and I. Pitas, “A new sparse image representation algorithm applied to facial expression recognition,” in Proceedings of the 14th IEEE Workshop on Machine Learning for Signal Processing (MLSP ’04), pp. 539-548, Sao Luis, Brazil, September-October 2004. · doi:10.1109/MLSP.2004.1423017
[10] P. O. Hoyer, “Non-negative matrix factorization with sparseness constraints,” Journal of Machine Learning Research, vol. 5, pp. 1457-1469, 2004. · Zbl 1222.68218
[11] Y. Xue, C. S. Tong, W.-S. Chen, and W. Zhang, “A modified non-negative matrix factorization algorithm for face recognition,” in Proceedings of the 18th International Conference on Pattern Recognition (ICPR ’06), vol. 3, pp. 495-498, Hong Kong, August 2006. · doi:10.1109/ICPR.2006.104
[12] S. Zafeiriou, A. Tefas, I. Buciu, and I. Pitas, “Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification,” IEEE Transactions on Neural Networks, vol. 17, no. 3, pp. 683-695, 2006. · doi:10.1109/TNN.2006.873291
[13] I. Buciu and I. Pitas, “NMF, LNMF, and DNMF modeling of neural receptive fields involved in human facial expression perception,” Journal of Visual Communication and Image Representation, vol. 17, no. 5, pp. 958-969, 2006. · Zbl 05461631 · doi:10.1016/j.jvcir.2006.06.001
[14] A. Pascual-Montano, J. M. Carazo, K. Kochi, D. Lehmann, and R. D. Pascual-Marqui, “Nonsmooth nonnegative matrix factorization (nsNMF),” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 3, pp. 403-415, 2006. · Zbl 05110885 · doi:10.1109/TPAMI.2006.60
[15] I. Kotsia, S. Zafeiriou, and I. Pitas, “A novel discriminant non-negative matrix factorization algorithm with applications to facial image characterization problems,” IEEE Transactions on Information Forensics and Security, vol. 2, no. 3, pp. 588-595, 2007. · doi:10.1109/TIFS.2007.902017
[16] C.-J. Lin, “Projected gradient methods for nonnegative matrix factorization,” Neural Computation, vol. 19, no. 10, pp. 2756-2779, 2007. · Zbl 1173.90583 · doi:10.1162/neco.2007.19.10.2756
[17] C.-J. Lin, “On the convergence of multiplicative update algorithms for nonnegative matrix factorization,” IEEE Transactions on Neural Networks, vol. 18, no. 6, pp. 1589-1596, 2007. · doi:10.1109/TNN.2007.895831
[18] T. Zhang, B. Fang, Y. Y. Tang, G. He, and J. Wen, “Topology preserving non-negative matrix factorization for face recognition,” IEEE Transactions on Image Processing, vol. 17, no. 4, pp. 574-584, 2008. · Zbl 05516610 · doi:10.1109/TIP.2008.918957
[19] B. B. Pan, W. S. Chen, and C. Xu, “Incremental learning of face recognition based on block non-negative matrix factorization,” 2008 (Chinese), to appear in Computer Application Research.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.