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Open AccessArticle

Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition

1
Department of Computer and Information Science, University of Konstanz, 78457 Konstanz, Germany
2
Department of Computer Science, Norwegian University of Science and Technology, N-2802 Gjøvik, Norway
*
Author to whom correspondence should be addressed.
This paper is an extended version of the conference paper: Jenadeleh, M.; Pedersen, M.; Saupe, D. Realtime quality assessment of iris biometrics in visible light. In Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA, 18–22 June 2018.
Sensors 2020, 20(5), 1308; https://doi.org/10.3390/s20051308
Received: 4 January 2020 / Revised: 23 February 2020 / Accepted: 25 February 2020 / Published: 28 February 2020
(This article belongs to the Special Issue Biometric Systems)
Image quality is a key issue affecting the performance of biometric systems. Ensuring the quality of iris images acquired in unconstrained imaging conditions in visible light poses many challenges to iris recognition systems. Poor-quality iris images increase the false rejection rate and decrease the performance of the systems by quality filtering. Methods that can accurately predict iris image quality can improve the efficiency of quality-control protocols in iris recognition systems. We propose a fast blind/no-reference metric for predicting iris image quality. The proposed metric is based on statistical features of the sign and the magnitude of local image intensities. The experiments, conducted with a reference iris recognition system and three datasets of iris images acquired in visible light, showed that the quality of iris images strongly affects the recognition performance and is highly correlated with the iris matching scores. Rejecting poor-quality iris images improved the performance of the iris recognition system. In addition, we analyzed the effect of iris image quality on the accuracy of the iris segmentation module in the iris recognition system. View Full-Text
Keywords: biometric recognition; visible light iris images; image quality assessment; image covariates; quality filtering biometric recognition; visible light iris images; image quality assessment; image covariates; quality filtering
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MDPI and ACS Style

Jenadeleh, M.; Pedersen, M.; Saupe, D. Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition. Sensors 2020, 20, 1308. https://doi.org/10.3390/s20051308

AMA Style

Jenadeleh M, Pedersen M, Saupe D. Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition. Sensors. 2020; 20(5):1308. https://doi.org/10.3390/s20051308

Chicago/Turabian Style

Jenadeleh, Mohsen; Pedersen, Marius; Saupe, Dietmar. 2020. "Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition" Sensors 20, no. 5: 1308. https://doi.org/10.3390/s20051308

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