Fusing the information in visible light and near-infrared images for iris recognition

Shamsafar, Faranak; Seyedarabi, Hadi; Aghagolzadeh, Ali

doi:10.1007/s00138-013-0572-3

Cited by 9 publications

(2 citation statements)

References 15 publications

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“…The implementation in the spatial domain is straightforward, such as weighted average and gradient transfer fusion [20]. The transform-domain based algorithms include nonsubsampled contourlet transform (NSCT) [2], wavelet [24], guided filter [30], etc. These transform image fusion methods are developed with the assumption that the IR and VI images are fully registered.…”

Section: Infrared and Visible Image Fusionmentioning

confidence: 99%

IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation

Liu

John

Blasch

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Context enhancement is critical for night vision (NV) applications, especially for the dark night situation without any artificial lights. In this paper, we present the infraredto-visual (IR2VI) algorithm, a novel unsupervised thermalto-visible image translation framework based on generative adversarial networks (GANs). IR2VI is able to learn the intrinsic characteristics from VI images and integrate them into IR images. Since the existing unsupervised GAN-based image translation approaches face several challenges, such as incorrect mapping and lack of fine details, we propose a structure connection module and a region-of-interest (ROI) focal loss method to address the current limitations. Experimental results show the superiority of the IR2VI algorithm over baseline methods.

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Section: Infrared and Visible Image Fusionmentioning

confidence: 99%

IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation

Liu

John

Blasch

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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“…In their work using 1D Log-Gabor features a feed forward neural network and the database in [1] is used. Shamsafar et al [20] sug- gested a Log-Gabor and Haar-based NIR and VW iris fusion method on the same UTIRIS database used in this paper.…”

Section: Related Workmentioning

confidence: 99%

On fusion for multispectral iris recognition

Wild

Radu

Ferryman

2015

2015 International Conference on Biometrics (ICB)

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Multispectral iris recognition uses information from multiple bands of the electromagnetic spectrum to better represent certain physiological characteristics of the iris texture and enhance obtained recognition accuracy. This paper addresses the questions of single versus crossspectral performance and compares score-level fusion accuracy for different feature types, combining different wavelengths to overcome limitations in less constrained recording environments. Further it is investigated whether Doddington's "goats" (users who are particularly difficult to recognize) in one spectrum also extend to other spectra. Focusing on the question of feature stability at different wavelengths, this work uses manual ground truth segmentation, avoiding bias by segmentation impact. Experiments on the public UTIRIS multispectral iris dataset using 4 feature extraction techniques reveal a significant enhancement when combining NIR + Red for 2-channel and NIR + Red + Blue for 3-channel fusion, across different feature types. Selective feature-level fusion is investigated and shown to improve overall and especially cross-spectral performance without increasing the overall length of the iris code.

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NIR/RGB image fusion for scene classification using deep neural networks

Soroush

Baleghi

2022

Vis Comput

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Fusing the information in visible light and near-infrared images for iris recognition

Cited by 9 publications

References 15 publications

IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation

IR2VI: Enhanced Night Environmental Perception by Unsupervised Thermal Image Translation

On fusion for multispectral iris recognition

NIR/RGB image fusion for scene classification using deep neural networks

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