| 1. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning
- 2019
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Ingår i: IEEE Access. - Piscataway, NJ : IEEE. - 2169-3536. ; 7, s. 6519-6544
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Tidskriftsartikel (refereegranskat)abstract
- The lack of resolution has a negative impact on the performance of image-based biometrics. While many generic super-resolution methods have been proposed to restore low-resolution images, they usually aim to enhance their visual appearance. However, an overall visual enhancement of biometric images does not necessarily correlate with a better recognition performance. Reconstruction approaches need thus to incorporate specific information from the target biometric modality to effectively improve recognition performance. This paper presents a comprehensive survey of iris super-resolution approaches proposed in the literature. We have also adapted an Eigen-patches reconstruction method based on PCA Eigentransformation of local image patches. The structure of the iris is exploited by building a patch-position dependent dictionary. In addition, image patches are restored separately, having their own reconstruction weights. This allows the solution to be locally optimized, helping to preserve local information. To evaluate the algorithm, we degraded high-resolution images from the CASIA Interval V3 database. Different restorations were considered, with 15 × 15 pixels being the smallest resolution evaluated. To the best of our knowledge, this is among the smallest resolutions employed in the literature. The experimental framework is complemented with six publicly available iris comparators, which were used to carry out biometric verification and identification experiments. Experimental results show that the proposed method significantly outperforms both bilinear and bicubic interpolation at very low-resolution. The performance of a number of comparators attain an impressive Equal Error Rate as low as 5%, and a Top-1 accuracy of 77-84% when considering iris images of only 15 × 15 pixels. These results clearly demonstrate the benefit of using trained super-resolution techniques to improve the quality of iris images prior to matching. © 2018, Emerald Publishing Limited.
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| 2. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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Eigen-patch iris super-resolution for iris recognition improvement
- 2015
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Ingår i: 2015 23rd European Signal Processing Conference (EUSIPCO). - Piscataway, NJ : IEEE Press. - 9780992862633 ; , s. 76-80
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Konferensbidrag (refereegranskat)abstract
- Low image resolution will be a predominant factor in iris recognition systems as they evolve towards more relaxed acquisition conditions. Here, we propose a super-resolution technique to enhance iris images based on Principal Component Analysis (PCA) Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information and reducing artifacts. We validate the system used a database of 1,872 near-infrared iris images. Results show the superiority of the presented approach over bilinear or bicubic interpolation, with the eigen-patch method being more resilient to image resolution reduction. We also perform recognition experiments with an iris matcher based 1D Log-Gabor, demonstrating that verification rates degrades more rapidly with bilinear or bicubic interpolation. ©2015 IEEE
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| 3. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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Improving Very Low-Resolution Iris Identification Via Super-Resolution Reconstruction of Local Patches
- 2017
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Ingår i: 2017 International Conference of the Biometrics Special Interest Group (BIOSIG). - Bonn : Gesellschaft für Informatik. - 9783885796640 - 9781538603963
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Konferensbidrag (refereegranskat)abstract
- Relaxed acquisition conditions in iris recognition systems have significant effects on the quality and resolution of acquired images, which can severely affect performance if not addressed properly. Here, we evaluate two trained super-resolution algorithms in the context of iris identification. They are based on reconstruction of local image patches, where each patch is reconstructed separately using its own optimal reconstruction function. We employ a database of 1,872 near-infrared iris images (with 163 different identities for identification experiments) and three iris comparators. The trained approaches are substantially superior to bilinear or bicubic interpolations, with one of the comparators providing a Rank-1 performance of ∼88% with images of only 15×15 pixels, and an identification rate of 95% with a hit list size of only 8 identities. © 2017 Gesellschaft fuer Informatik.
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| 4. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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Iris Super-Resolution Using Iterative Neighbor Embedding
- 2017
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Ingår i: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. - Los Alamitos : IEEE Computer Society. - 9781538607336 - 9781538607343 ; , s. 655-663
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Konferensbidrag (refereegranskat)abstract
- Iris recognition research is heading towards enabling more relaxed acquisition conditions. This has effects on the quality and resolution of acquired images, severely affecting the accuracy of recognition systems if not tackled appropriately. In this paper, we evaluate a super-resolution algorithm used to reconstruct iris images based on iterative neighbor embedding of local image patches which tries to represent input low-resolution patches while preserving the geometry of the original high-resolution space. To this end, the geometry of the low- and high-resolution manifolds are jointly considered during the reconstruction process. We validate the system with a database of 1,872 near-infrared iris images, while fusion of two iris comparators has been adopted to improve recognition performance. The presented approach is substantially superior to bilinear/bicubic interpolations at very low resolutions, and it also outperforms a previous PCA-based iris reconstruction approach which only considers the geometry of the low-resolution manifold during the reconstruction process. © 2017 IEEE
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| 5. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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Learning-Based Local-Patch Resolution Reconstruction of Iris Smart-phone Images
- 2017
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Konferensbidrag (refereegranskat)abstract
- Application of ocular biometrics in mobile and at a distance environments still has several open challenges, with the lack quality and resolution being an evident issue that can severely affects performance. In this paper, we evaluate two trained image reconstruction algorithms in the context of smart-phone biometrics. They are based on the use of coupled dictionaries to learn the mapping relations between low and high resolution images. In addition, reconstruction is made in local overlapped image patches, where up-scaling functions are modelled separately for each patch, allowing to better preserve local details. The experimental setup is complemented with a database of 560 images captured with two different smart-phones, and two iris comparators employed for verification experiments. We show that the trained approaches are substantially superior to bilinear or bicubic interpolations at very low resolutions (images of 13×13 pixels). Under such challenging conditions, an EER of ∼7% can be achieved using individual comparators, which is further pushed down to 4-6% after the fusion of the two systems. © 2017 IEEE
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| 6. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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Reconstruction of Smartphone Images for Low Resolution Iris Recognition
- 2015
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Ingår i: 2015 IEEE International Workshop on Information Forensics and Security (WIFS). - Piscataway, NJ : IEEE Press. - 9781467368025
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Konferensbidrag (refereegranskat)abstract
- As iris systems evolve towards a more relaxed acquisition, low image resolution will be a predominant issue. In this paper we evaluate a super-resolution method to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. We employ a database of 560 images captured in visible spectrum with two smartphones. The presented approach is superiorto bilinear or bicubic interpolation, especially at lower resolutions. We also carry out recognition experiments with six iris matchers, showing that better performance can be obtained at low-resolutions with the proposed eigen-patch reconstruction, with fusion of only two systems pushing the EER to below 5-8% for down-sampling factors up to a size of only 13x13. © 2015 IEEE.
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| 7. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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Super-Resolution for Selfie Biometrics : Introduction and Application to Face and Iris
- 2019. - 1
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Ingår i: Selfie Biometrics. - Cham : Springer. - 9783030269715 - 9783030269722 ; , s. 105-128
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Bokkapitel (refereegranskat)abstract
- Biometric research is heading towards enabling more relaxed acquisition conditions. This has effects on the quality and resolution of acquired images, severly affecting the accuracy of recognition systems if not tackled appropriately. In this chapter, we give an overview of recent research in super-resolution reconstruction applied to biometrics, with a focus on face and iris images in the visible spectrum, two prevalent modalities in selfie biometrics. After an introduction to the generic topic of super-resolution, we investigate methods adapted to cater for the particularities of these two modalities. By experiments, we show the benefits of incorporating super-resolution to improve the quality of biometric images prior to recognition. © Springer Nature AG 2019
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| 8. |
- Alonso-Fernandez, Fernando, 1978-, et al.
(författare)
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Very Low-Resolution Iris Recognition Via Eigen-Patch Super-Resolution and Matcher Fusion
- 2016
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Ingår i: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS). - Piscataway : IEEE. - 9781467397339 - 9781467397346
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Konferensbidrag (refereegranskat)abstract
- Current research in iris recognition is moving towards enabling more relaxed acquisition conditions. This has effects on the quality of acquired images, with low resolution being a predominant issue. Here, we evaluate a super-resolution algorithm used to reconstruct iris images based on Eigen-transformation of local image patches. Each patch is reconstructed separately, allowing better quality of enhanced images by preserving local information. Contrast enhancement is used to improve the reconstruction quality, while matcher fusion has been adopted to improve iris recognition performance. We validate the system using a database of 1,872 near-infrared iris images. The presented approach is superior to bilinear or bicubic interpolation, especially at lower resolutions, and the fusion of the two systems pushes the EER to below 5% for down-sampling factors up to a image size of only 13×13.
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| 9. |
- Ribeiro, Eduardo, et al.
(författare)
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Exploring Deep Learning Image Super-Resolution for Iris Recognition
- 2017
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Ingår i: 25th European Signal Processing Conference (EUSIPCO 2017). - : Institute of Electrical and Electronics Engineers (IEEE). - 9780992862671 - 9780992862688 - 9781538607510 ; , s. 2176-2180
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Konferensbidrag (refereegranskat)abstract
- In this work we test the ability of deep learning methods to provide an end-to-end mapping between low and high resolution images applying it to the iris recognition problem. Here, we propose the use of two deep learning single-image super-resolution approaches: Stacked Auto-Encoders (SAE) and Convolutional Neural Networks (CNN) with the most possible lightweight structure to achieve fast speed, preserve local information and reduce artifacts at the same time. We validate the methods with a database of 1.872 near-infrared iris images with quality assessment and recognition experiments showing the superiority of deep learning approaches over the compared algorithms. © EURASIP 2017.
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