Abstract
The coupled metric learning is a novel metric method to solve the matching problem of the elements in different data sets. In this paper, we improved the supervised locality preserving projection algorithm, and added within-class and between-class information of this algorithm to coupled metric learning, so a novel coupled metric learning method is proposed. This method can effectively extract the nonlinear feature information, and the operation is simple. The experiments based on two face databases are performed. The results show that, the proposed method can get higher recognition rate in low-resolution and fuzzy face recognition, and can reduce the computing time; it is an effective metric method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Davis, J.V., Kulis, B., Jain, P., et al.: Information-theoretic metric learning. In: The 24th International Conference on Machine Learning, pp. 209–216. ACM Press, Oregon (2007)
Shiming, X., Feiping, N., Changshui, Z.: Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognition 41(12), 3600–3612 (2008)
Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. The Journal of Machine Learning Research 10, 207–244 (2009)
Kulis, B., Jain, P., Grauman, K.: Fast similarity search for learned metrics. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(12), 2143–2157 (2009)
Zhengping, H., Liang, L., Chengqian, X., et al.: Sparse Distance Metric Learning with L1-Norm Constraint for One-Class Samples in High-Dimensional Space and Its Application. Mathematics in Practice and Theory 41(6), 116–124 (2011)
Lei, W., Tie, L., Huading, J.: Chunk Incremental Distance Metric Learning Algorithm Based on Manifold Regularization. Acta Electronic Sinica 39(5), 1131–1135 (2011)
Mahdieh, S.B., Saeed, B.S.: Kernel-based metric learning for semi-supervised clustering. Neurocomputing 73, 1352–1361 (2010)
Hotelling, H.: Analysis of A Complex of Statistical Variables into Principal Components. Journal of Educational Psychology 24, 417–441 (1933)
Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7, 179–188 (1936)
Comon, P., et al.: Independent Components Analysis, a New Concept. Signal Procssing 36(3), 287–314 (1994)
Roweis, S.T., Saul, L.K.: Nonlinear Dimensionality Reduction by Locally Linear Embedding. Science 290, 2323–2326 (2000)
Tenenbaum, J., Silva, V., Langford, J.: A Global Geometric Framework for Nonlinear Dimensionality Reduction. Science 290, 2319–2323 (2000)
Belkin, M., Niyogi, P.: Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering. Advances in Neural Information Processing System 14, 585–591 (2002)
Li, B., Chang, H., Shan, S., Chen, X.: Coupled Metric Learning for Face Recognition with Degraded Images. In: Zhou, Z.-H., Washio, T. (eds.) ACML 2009. LNCS, vol. 5828, pp. 220–233. Springer, Heidelberg (2009)
Xianye, B., Weixiao, M., Rui, Y., et al.: An improved biometrics technique based on metric learning approach. Neurocomputing 79(11), 44–51 (2012)
Li, B., Chang, H., Shan, S., et al.: Low-Resolution Face Recognition via Coupled Locality Preserving Mappings. IEEE Signal Processing Letters 17(1), 20–23 (2010)
Zhonghua, S., Yonghui, P., et al.: A Supervised Locality Preserving Projection Algorithm for Dimensionality Reduction. Pattern Recognition and Artificial Intelligence 21(2), 233–239 (2008)
Yuanyuan, W., Zhimin, S., Zheng, C., et al.: Super-resolution image restoration based on maximum likelihood estimation. Chinese Journal of Scientific Instrument 29(5), 949–953 (2008)
Xiaofei, Y., Fujie, C., Xuejun, Y.: Template matching by wiener filtering. Journal of Computer Research & Development 37(12), 1499–1503 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
Zou, G., Jiang, S., Zhang, Y., Fu, G., Wang, K. (2013). A Novel Coupled Metric Learning Method and Its Application in Degraded Face Recognition. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds) Biometric Recognition. CCBR 2013. Lecture Notes in Computer Science, vol 8232. Springer, Cham. https://doi.org/10.1007/978-3-319-02961-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-319-02961-0_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02960-3
Online ISBN: 978-3-319-02961-0
eBook Packages: Computer ScienceComputer Science (R0)