Cross-sensor iris verification applying robust fused segmentation algorithms

Llano, Eduardo Garea; Vargas, J. Mario; García-Vázquez, Mireya S.; Fuentes, Luis Miguel Zamudio; Ramírez-Acosta, Alejandro A.

doi:10.1109/icb.2015.7139042

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…Jillela and Ross [20] proposed image-level fusion with Principal Components Transform. Recently, Llano et al [8] investigate the positive segmentation impact of PCAbased fusion vs. Laplacian Pyramid and Exponential Mean at image-level, i.e. multiple normalised iris textures are fused retrieved by following different segmentation algorithms.…”

Section: Related Workmentioning

confidence: 99%

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Segmentation-Level Fusion for Iris Recognition

Wild

Hofbauer

Ferryman

et al. 2015

2015 International Conference of the Biometrics Special Interest Group (BIOSIG)

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This paper investigates the potential of fusion at normalisation/segmentation level prior to feature extraction. While there are several biometric fusion methods at data/feature level, score level and rank/decision level combining raw biometric signals, scores, or ranks/decisions, this type of fusion is still in its infancy. However, the increasing demand to allow for more relaxed and less invasive recording conditions, especially for onthe-move iris recognition, suggests to further investigate fusion at this very low level. This paper focuses on the approach of multi-segmentation fusion for iris biometric systems investigating the benefit of combining the segmentation result of multiple normalisation algorithms, using four methods from two different public iris toolkits (USIT, OSIRIS) on the public CASIA and IITD iris datasets. Evaluations based on recognition accuracy and ground truth segmentation data indicate high sensitivity with regards to the type of errors made by segmentation algorithms.

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Section: Related Workmentioning

confidence: 99%

“…image-based and binary features [7]). Fusion at image data level, such as in [8] following [9] reveals promising results, but requires the multiple execution of the iris unwrapping . .…”

Section: Introductionmentioning

confidence: 99%

Segmentation-Level Fusion for Iris Recognition

Wild

Hofbauer

Ferryman

et al. 2015

2015 International Conference of the Biometrics Special Interest Group (BIOSIG)

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“…More specifically, in contrast to [18], a single image only is required and unlike [19] normalisation is executed only once. According to our knowledge, this paper is the first to present a quality-driven fusion at segmentationlevel in iris recognition.…”

Section: Iris Segmentation Fusionmentioning

confidence: 99%

“…Segmentation fusion can be grouped into approaches combining detected boundaries prior to any rubbersheet transformation [4,5] and after normalisation, where normalised texture is combined [18,19]. The latter requires multiple execution of the iris unwrapping and normalisation (slower), hiding potential segmentation errors and therefore making their elimination more complex (combination of texture).…”

Section: Iris Segmentation Fusionmentioning

confidence: 99%

“…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%

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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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Consensual Iris Segmentation Fusion

Roig

Llano

2017

Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

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Cross-sensor iris verification applying robust fused segmentation algorithms

Cited by 10 publications

References 9 publications

Segmentation-Level Fusion for Iris Recognition

Segmentation-Level Fusion for Iris Recognition

Quality-based iris segmentation-level fusion

Consensual Iris Segmentation Fusion

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