Information fusion in low-resolution iris videos using Principal Components Transform

Jillela, Raghavender; Ross, Arun; Flynn, Patrick J.

doi:10.1109/wacv.2011.5711512

Cited by 23 publications

(14 citation statements)

References 9 publications

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“…Most of them implement data-level fusion for superresolution from multiple video frames, such as [20,21]. State-of-the-art in this context are principal components transform [2,18] combining multiple normalised iris textures at image-level obtained by different segmentation algorithms. As representative of the first group, Uhl et al [4] suggested different strategies to combine direct segmentation boundaries rather than texture feeding the combined model into the normalisation routine.…”

Section: Iris Segmentation Fusionmentioning

confidence: 99%

“…Among these tasks, it is especially iris localisation and pupillary/limbic boundary detection which challenge existing implementations [2], at least for images captured under less ideal conditions. Examples of undesirable conditions are visible light imaging with weak pupillary boundaries, on-the-move near infrared acquisition with typical motion blur, out-of-focus images, or images with weak limbic contrast.…”

Section: Introductionmentioning

confidence: 99%

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Quality-based iris segmentation-level fusion

Wild

Hofbauer

Ferryman

et al. 2016

EURASIP J. on Info. Security

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Iris localisation and segmentation are challenging and critical tasks in iris biometric recognition. Especially in non-cooperative and less ideal environments, their impact on overall system performance has been identified as a major issue. In order to avoid a propagation of system errors along the processing chain, this paper investigates iris fusion at segmentation-level prior to feature extraction and presents a framework for this task. A novel intelligent reference method for iris segmentation-level fusion is presented, which uses a learning-based approach predicting ground truth segmentation performance from quality indicators and model-based fusion to create combined boundaries. The new technique is analysed with regard to its capability to combine segmentation results (pupillary and limbic boundaries) of multiple segmentation algorithms. Results are validated on pairwise combinations of four open source iris segmentation algorithms with regard to the public CASIA and IITD iris databases illustrating the high versatility of the proposed method.

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Section: Iris Segmentation Fusionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quality-based iris segmentation-level fusion

Wild

Hofbauer

Ferryman

et al. 2016

EURASIP J. on Info. Security

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“…Since Baker and Kanade first suggested SR algorithms specifically for faces [5], there has been a lot of research in face super-resolution (also called hallucination) [6]. Learning approaches have been also used with other biometrics including iris [7], [8], [9], [10]. However, one major limitation is that they try to develop a prototype iris using combination of complete images.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of smartphone images for low resolution iris recognition

Alonso‐Fernandez

Farrugia

Bigün

2015

2015 IEEE International Workshop on Information Forensics and Security (WIFS)

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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 superior to bilinear or bicubic interpolation, specially 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 13×13.

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“…Note that they considered a protocol similar to MBGC, where they compare a video to a high quality still image. More recent works [7,8] explored the fusion in the feature domain using PCA or PCT but not on the same MBGC protocol as they usually degrade artificially the image resolution in their assessment stage.…”

Section: Introductionmentioning

confidence: 99%

Quality-Based Super Resolution for Degraded Iris Recognition

Othman

Houmani

Dorizzi

2014

Advances in Intelligent Systems and Computing

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In this paper we address the problem of low-quality iris recognition via super resolution approaches. We introduce two novel quality measures, one computed 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 one considers 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 fusion outperforms 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 v4 database containing sequences of still images with degraded quality resulting in severe 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.

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Information fusion in low-resolution iris videos using Principal Components Transform

Cited by 23 publications

References 9 publications

Quality-based iris segmentation-level fusion

Quality-based iris segmentation-level fusion

Reconstruction of smartphone images for low resolution iris recognition

Quality-Based Super Resolution for Degraded Iris Recognition

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