Abstract
Iris recognition is a widely used biometric identification technology that relies on the accurate and efficient matching of iris images. However, fast matching in large databases poses a significant challenge due to the increasing search time for a given query. To address this problem, this paper proposes an end-to-end hashing framework for iris recognition tasks based on the DenseFly algorithm. The presented approach utilizes a deep convolutional neural network to extract features from iris images and then applies hashing to map the features into compact binary codes. This process enables efficient retrieval of the query iris templates by reducing the whole search space. To evaluate and compare the proposed method with the existing IHashNet approach, we conduct experiments on three publicly available iris datasets namely CASIA-Irisv4-Thousand, UBIRIS.v2 and CASIA-Irisv4-Lamp. Our simulation results demonstrate that the proposed method outperforms IHashNet in terms of retrieval accuracy and equal error rate (EER). Furthermore, our method achieves significantly lower query time over all the datasets, thereby vindicating its usage over large iris datasets.
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
Notes
- 1.
- 2.
CASIA Iris Image Database, http://biometrics.idealtest.org/.
References
Ahmed, T., Sarma, M.: Hash-based space partitioning approach to iris biometric data indexing. Expert Syst. Appl. 134, 1–13 (2019). https://doi.org/10.1016/j.eswa.2019.05.026, https://www.sciencedirect.com/science/article/pii/S0957417419303550
Indyk, P., Motwani, R.: Approximate nearest neighbors: towards removing the curse of dimensionality. In: Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, pp. 604–613. STOC 1998, Association for Computing Machinery, New York, NY, USA (1998). https://doi.org/10.1145/276698.276876
Lv, Q., Josephson, W., Wang, Z., Charikar, M., Li, K.: Multi-probe LSH: efficient indexing for high-dimensional similarity search. In: 33rd International Conference on Very Large Data Bases, VLDB 2007 - Conference Proceedings, pp. 950–961. Association for Computing Machinery, Inc. (2007)
Nazmdeh, V., Mortazavi, S., Tajeddin, D., Nazmdeh, H., Asem, M.M.: Iris recognition; from classic to modern approaches. In: 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0981–0988 (2019). https://doi.org/10.1109/CCWC.2019.8666516
Proenca, H., Filipe, S., Santos, R., Oliveira, J., Alexandre, L.A.: The UBIRIS.V2: a database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1529–1535 (2010). https://doi.org/10.1109/TPAMI.2009.66
Proenca, H.: Iris recognition: on the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1502–1516 (2010). https://doi.org/10.1109/TPAMI.2009.140
Sharma, J., Navlakha, S.: Improving similarity search with high-dimensional locality-sensitive hashing (2018)
Singh, A., Gaurav, P., Vashist, C., Nigam, A., Yadav, R.P.: Ihashnet: Iris hashing network based on efficient multi-index hashing. In: 2020 IEEE International Joint Conference on Biometrics (IJCB), pp. 1–9 (2020). https://doi.org/10.1109/IJCB48548.2020.9304925
Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5265–5274 (2018). https://doi.org/10.1109/CVPR.2018.00552
Weiss, Y., et al. (eds.) Advances in Neural Information Processing Systems. vol. 21. Curran Associates, Inc. (2008). https://proceedings.neurips.cc/paper_files/paper/2008/file/d58072be2820e8682c0a27c0518e805e-Paper.pdf
Wen, Y., Zhang, K., Li, Z., Qiao, Yu.: A discriminative feature learning approach for deep face recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 499–515. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46478-7_31
Zhang, W., Lu, X., Gu, Y., Liu, Y., Meng, X., Li, J.: A robust iris segmentation scheme based on improved u-net. IEEE Access 7, 85082–85089 (2019). https://doi.org/10.1109/ACCESS.2019.2924464
Acknowledgements
This work is supported by the Start-up Research Grant (SRG), Science and Engineering Research Board (SERB), Government of India [Grant Number(s): SRG/2021/000051 and SRG/2021/000173].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Singh, A.P., Sadhya, D., Rathore, S.S. (2023). Fast Similarity Search in Large-Scale Iris Databases Using High-Dimensional Hashing. In: Goyal, V., Kumar, N., Bhowmick, S.S., Goyal, P., Goyal, N., Kumar, D. (eds) Big Data and Artificial Intelligence. BDA 2023. Lecture Notes in Computer Science, vol 14418. Springer, Cham. https://doi.org/10.1007/978-3-031-49601-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-49601-1_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49600-4
Online ISBN: 978-3-031-49601-1
eBook Packages: Computer ScienceComputer Science (R0)