Abstract
In this paper, a new algorithm named Adaptive Weighted Label Propagation (AWLP) which explores the complementary property among sub-patterns from the same face image is proposed for local matching based face recognition. The proposed AWLP first partitions the face images into several smaller sub-images. Then, multiple similarity graphs are constructed for different sub-pattern sets. At last, in order to take correlation among different sub-patterns into account, the graphs obtained by various sub-pattern sets are combined and the procedures of label prediction and graph weight learning are integrated into a unified framework to propagate the class information of the labeled samples to unlabeled ones. Moreover, a simple yet efficient iterative update algorithm is also proposed to solve our AWLP. Extensive experiments on three face benchmark databases show that AWLP has very competitive performance with the state-of-the-art algorithms.
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.
References
Li, S.Z., Jain, A.K.: Handbook of face recognition. Springer, New York (2011)
Belkin, M., Niyogi, P., Sindhwani, V.: Manifold regularization: A geometric framework for learning from labeled and unlabeled examples. The Journal of Machine Learning Research 7, 2399–2434 (2006)
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: NIPS, pp. 321–328 (2004)
Wang, J., Wang, F., Zhang, C., Shen, H.C., Quan, L.: Linear neighborhood propagation and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 31(9), 1600–1615 (2009)
Cheng, H., Liu, Z., Yang, J.: Sparsity induced similarity measure for label propagation. In: ICCV, pp. 317–324 (2009)
Zang, F., Zhang, J.S.: Label propagation through sparse neighborhood and its applications. Neurocomputing 97, 267–277 (2012)
Tian, Z., Kuang, R.: Global Linear Neighborhoods for Efficient Label Propagation. In: SDM, pp. 863–872 (2013)
Chen, Y., Li, Z.Z., Jin, Z.: Feature extraction based on maximum nearest subspace margin criterion. Neural Processing Letters 37(3), 355–375 (2013)
Wang, J.Z., Ma, Z.Q., Zhang, B.X., Qi, M., Kong, J.: A structure-preserved local match-ing approach for face recognition. Pattern Recognition Letters 32(3), 494–504 (2011)
Xia, T., Tao, D., Mei, T., Zhang, Y.: Multiview spectral embedding. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 40(6), 1438–14469 (2010)
Yale University Face Database (2002)
Martinez, A., Benavente, R.: The AR Face Database, CVC Technical Report 24 (1998)
Terence, S., Simon, B., Maan, B.: The CMU pose, illumination, and expression (PIE) database. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12), 1615–1618 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Guo, Y., Li, X., Yi, Y., Wei, Y., Wang, J. (2014). Adaptive Weighted Label Propagation for Local Matching Based Face Recognition. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds) Biometric Recognition. CCBR 2014. Lecture Notes in Computer Science, vol 8833. Springer, Cham. https://doi.org/10.1007/978-3-319-12484-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-12484-1_8
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12483-4
Online ISBN: 978-3-319-12484-1
eBook Packages: Computer ScienceComputer Science (R0)