Abstract
In this paper, the influence of dense coding on the recognition of offset-image is explored by improving the feature extraction ability of network model. By the experimental results, we find that adding the PCANet convolution layer to obtain more filters cannot effectively improve the network recognition ability of the offset-image recognition. Based on this background, we propose a new dense network framework on the basis of the FPH framework. Experimental results on the AR dataset and MNIST variations show that the proposed network framework is effective and improves significantly the ability of the offset-image recognition.
This work was funded by the Natural Science Foundation of China under grant number 61872422, and the Natural Science Foundation of Zhejiang Province, China under great number LY19F020028.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2892–2900. IEEE, Boston (2015)
Lui, Y.M., Bolme, D., Phillips, P.J., et al.: Preliminary studies on the good, the bad, and the ugly face recognition challenge problem. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 9–16. IEEE, Providence (2012)
Wu, X., He, R., Sun, Z.: A lightened CNN for deep face representation. https://arxiv.org/abs/1511.02683. Accessed 15 Nov 2015
Li, X., Liang, R.: A survey of occluded face recognition: from subspace regression to deep learning. Acta Sinica Sinica 41(1), 1–28 (2017)
Chan, T.H., Jia, K., Gao, S., et al.: PCANet: a simple deep learning baseline for image classification? IEEE T. Image Process. 24(12), 5017–5032 (2015)
Wright, J., Ma, Y.: Dense error correction via l1-minimization. IEEE T. Inform. Theory 56(7), 3540–3560 (2010)
Ojala, T., Pietikainen, M.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE T. Pattern Anal. 24(7), 971–987 (2002)
Zhang, B., Shan, S., Chen, X., et al.: Histogram of gabor phase patterns (HGPP): a novel object representation approach for face recognition. IEEE T. Image Process. 16(1), 57–68 (2007)
Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE T. Pattern Anal. 15(11), 1148–1161 (1993)
Martinez, A., Benavente, R.: The AR face database. Technical report, Purdue University West Lafayette (1998)
Huang, G.B., Mattar, M., Berg, T., et al.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report, University of Massschusetts (2008)
Huang, G.B., Mattar, M., Lee, H., et al.: Learning to align from scratch. In: Proceedings of the Advances in Neural Information Processing Systems, pp. 773–781. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Cao, J. et al. (2021). The Offset-Image Recognition Based on Dense Coding. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_45
Download citation
DOI: https://doi.org/10.1007/978-3-030-78609-0_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78608-3
Online ISBN: 978-3-030-78609-0
eBook Packages: Computer ScienceComputer Science (R0)