Abstract
In this paper we address the problem of low-quality iris recognition via super resolution approaches. We introduce two novel quality measures, onecomputed Globally (GQ) and the other Locally (LQ), for fusing at the pixel level(after a bilinear interpolation step) the images corresponding to several shots of a given person. These measures derive from a local GMM probabilistic characterization of good quality iris texture. We performed two types of experiments. The first oneconsiders low resolution video sequences coming from the MBGC portal database: it shows the superiority of our approach compared to score-based or average image-based fusion methods. Moreover, we show that the LQ-based fusionoutperforms the GQ-based fusion with a relative improvement of 4.79 % at the Equal Error Rate functioning point. The second experiment is performed on CASIA v4database containing sequences of still images with degraded quality resulting insevere segmentation errors. We show that the image fusion scheme improves greatly the performance and that the LQ-based fusion is mainly interesting for low FAR values.
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
References
MBGC portal challenge version 2 preliminary results. http://www.nist.gov/itl/iad/ig/mbgc-presentations.cfm
Hollingsworth, K., Peters, T., Bowyer, K.W., Flynn, P.J.: Iris recognition using signal-level fusion of frames from video. In: IEEE Transactions on Information Forensics and Security, vol. 4, no. 4, pp. 837–848 (2009)
Fahmy, G.: Super-resolution construction of IRIS images from a visual low resolution face video. In: Proceedings of the International Symposium on Signal Processing and Its Applications (2007)
Nguyen, K., Fookes, C.B., Sridharan, S., Denman, S.: Focus-score weighted super-resolution for uncooperative iris recognition at a distance and on the move. In: Proceedings of the International Conference of Image and Vision Computing (2010)
Nguyen, K., Fookes, C.B., Sridharan, S., Denman, S.: Quality-driven super-resolution for less constrained iris recognition at a distance and on the move. In: IEEE Transactions on Information Forensics and Security, vol. 6, no. 4, pp. 1248–1258 (2011)
Nguyen, K., Fookes, C.B., Sridharan, S., Denman, S.: Feature-domain super-resolution for iris recognition. In: Proceedings of the International Conference on Image Processing (2011)
Jillela, R., Ross, A., Flynn, P.J.: Information fusion in low-resolution iris videos using principal components transform. In: Proceedings of the 2011 IEEE Workshop on Applications of Computer Vision, pp. 262–269 (2011)
Cremer, S., Dorizzi, B., Garcia-Salicetti S., Lempiérière, N.: How a local quality measure can help improving iris recognition. In: Proceedings of the International Conference of the Biometrics Special Interest Group (2012)
Krichen, E., Garcia-Salicetti, S., Dorizzi, B.: A new probabilistic iris quality measure for comprehensive noise detection. In: Proceedings of the IEEE International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–6 (2007)
Daugman, J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14, 21–30 (2007)
BioSecure project: http://biosecure.it-sudparis.eu
Sutra, G., Garcia-Salicetti, S., Dorizzi, B.: The viterbi algorithm at different resolutions for enhanced iris segmentation. In: Proceedings of the International Conference on Biometrics, pp. 310–316 (2012)
Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process. 13, 739–750 (2004)
Krichen, E., Allano, L., Garcia-Salicetti, S., Dorizzi, B.: Specific texture analysis for iris recognition. Audio- and video-based biometric person authentication, pp. 23–30. Springer, Berlin (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Othman, N., Houmani, N., Dorizzi, B. (2015). Quality-Based Super Resolution for Degraded Iris Recognition. In: Fred, A., De Marsico, M. (eds) Pattern Recognition Applications and Methods. Advances in Intelligent Systems and Computing, vol 318. Springer, Cham. https://doi.org/10.1007/978-3-319-12610-4_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-12610-4_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12609-8
Online ISBN: 978-3-319-12610-4
eBook Packages: EngineeringEngineering (R0)