Abstract
The available images of biometrics recognition system in real-world applications are often degraded and of low-resolution, making the acquired images contain less detail information. Therefore, biometrics recognition of the low-resolution image is a challenging problem. It has received increasing attention in recent years. In this paper, a two-step ear recognition scheme based on super-resolution is proposed, which will contribute to both human-based and machine-based recognition. Unlike most standard super-resolution methods which aim to improve the visual quality of ordinary images, the proposed super-resolution based method is designed to improve the recognition performance of low-resolution ear image, which uses LC-KSVD algorithm to learn much more discriminative atoms of the dictionary. When applied to low-resolution ear recognition problem, the proposed method achieves better recognition performance compared with the present super-resolution 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
Jain, A.K., Flynn, P., Ross, A.A.: Handbook of Biometrics. Springer (2007)
Mu, Z., Yuan, L., Xu, Z., Xi, D., Qi, S.: Shape and Structural Feature Based Ear Recognition. In: Proceedings of the 5th Chinese Conference on Biometric Recognition, Guangzhou, China, pp. 663–670 (2004)
Zhang, B., Mu, Z., Li, C., et al.: Robust Classification for Occluded Ear via Gabor Scale Feature-Based Non-negative Sparse Representation. Optical Engineering 53(6), 061702 (2013)
Li, B., Chang, H., Shan, S., et al.: Low-resolution Face Recognition via Coupled Locality Preserving Mappings [J]. IEEE Signal Processing Letters 17(1), 20–23 (2010)
Baker, S., Kanade, T.: Hallucinating Faces. In: Proceedings. Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000, pp. 83–88. IEEE (2000)
Freeman, W.T., Pasztor, E.C., Carmichael, O.T.: Learning Low-level Vision [J]. International Journal of Computer Vision 40(1), 25–47 (2000)
Wang, X., Tang, X.: Hallucinating Face by Eigentransformation. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 35(3), 425–434 (2005)
Yang, J., Wright, J., Huang, T.S., et al.: Image Super-resolution via Sparse Representation. IEEE Transactions on Image Processing 19(11), 2861–2873 (2010)
Zeyde, R., Elad, M., Protter, M.: On single image scale-up using sparse-representations. In: Boissonnat, J.-D., Chenin, P., Cohen, A., Gout, C., Lyche, T., Mazure, M.-L., Schumaker, L. (eds.) Curves and Surfaces 2011. LNCS, vol. 6920, pp. 711–730. Springer, Heidelberg (2012)
Timofte, R., Smet, V.D., Gool, L.V.: Anchored Neighborhood Regression for Fast Example-based Super-resolution. In: IEEE Int. Conf. Computer Vision (2013)
Jiang, Z., Lin, Z., Davis, L.S.: Label Consistent K-SVD: Learning A Discriminative Dictionary for Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(11), 2651–2664 (2013)
Baraniuk, R.G.: Compressive sensing. IEEE Signal Processing Magazine 24(4) (2007)
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
Luo, S., Mu, Z., Zhang, B. (2014). Discriminative Super-Resolution Method for Low-Resolution Ear 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_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-12484-1_50
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12483-4
Online ISBN: 978-3-319-12484-1
eBook Packages: Computer ScienceComputer Science (R0)