Iris Image Retrieval Based on Macro-features

Sunder, Manisha Sam; Ross, Arun

doi:10.1109/icpr.2010.328

Cited by 23 publications

(8 citation statements)

References 9 publications

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“…The detected features are used for iris matching and retrieval. However, crypts are not considered in [17].…”

Section: Introductionmentioning

confidence: 97%

“…J. Mira et al [11] use morphology operations to highlight texture patterns that are similar to fingerprint ridges, and nodes and end-points of the ridges are used to discriminate different eyes as minutiae. Sunder [17] applies SIFT [9] features to detect macrofeatures such as moles, freckles, nevi, melanoma, etc. on images acquired under illuminations of visible wavelengths.…”

Section: Introductionmentioning

confidence: 99%

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Iris crypts: Multi-scale detection and shape-based matching

Shen

Flynn

2014

IEEE Winter Conference on Applications of Computer Vision

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This paper presents an improved framework for iris crypt detection and matching that outperforms both previous methods and manual annotations. The system uses a multi-scale pyramid architecture to detect feature candidates before they are further examined and optimized by heuristic-based methods. The dissimilarity between irises are measured by a two-stage matcher in the simple to complex order. The first stage estimates the global dissimilarity and rejects the majority of unmatching candidates. The surviving pairs are matched by local dissimilarities between each crypt pair using shape descriptors. The proposed framework showed significant performance improvement in both identification and verification context.

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“…The detected features are used for iris matching and retrieval. However, crypts are not considered in [17].…”

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Iris crypts: Multi-scale detection and shape-based matching

Shen

Flynn

2014

IEEE Winter Conference on Applications of Computer Vision

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“…A typical iris recognition system captures frontal images of the eye in the near-infrared (NIR) spectrum. However, recently, researchers have attempted to perform iris recognition in the visible spectrum, in non-cooperative scenarios with relaxed constraints [1][2][3][4][5][6][7][8] . Competitions such as NICE I and II, have encouraged researchers to develop effective algorithms for iris segmentation and feature extraction in the visible spectrum.…”

Section: Introductionmentioning

confidence: 99%

An indexing method for color iris images

Crihalmeanu

Ross

2015

SPIE Proceedings

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In this work, we study the possibility of indexing color iris images. In the proposed approach, a clustering scheme on a training set of iris images is used to determine cluster centroids that capture the variations in chromaticity of the iris texture. An input iris image is indexed by comparing its pixels against these centroids and determining the dominant clusters -i.e., those clusters to which the majority of its pixels are assigned to. The cluster indices serve as an index code for the input iris image and are used during the search process, when an input probe has to be compared with a gallery of irides. Experiments using multiple color spaces convey the efficacy of the scheme on good quality images, with hit rates closes to 100% being achieved at low penetration rates.

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“…De Mira et al [7] use morphological operators to highlight ridge-like patterns and match irises by the extracted nodes, end-points and branches. Sunder et al [10] employ SIFT to characterize the macro-features on the iris and use them for template retrieval. Unfortunately, none of the foregoing methods conclusively correlate the extracted feature with any specific types of visible features.…”

Section: Introductionmentioning

confidence: 99%

Are iris crypts useful in identity recognition?

Shen

Flynn

2013

2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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We conducted an experiment in which participants were asked to annotate the crypts they see in iris images. The results were used to assess the utility of crypts in identity recognition for forensic applications. Although the inter-participant annotation consistency may be limited by crypts' noticeability and the range of genuine similarity scores obtained by the adopted matcher is correlated to the number of crypt pixels in the given image, the intra-iris crypt perception is sufficiently consistent for nearly 95% of query images to be correctly identified. The performance is expected to be better for the professional examiners with comprehensive training.

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Iris Image Retrieval Based on Macro-features

Abstract: Abstract

Cited by 23 publications

References 9 publications

Iris crypts: Multi-scale detection and shape-based matching

Iris crypts: Multi-scale detection and shape-based matching

An indexing method for color iris images

Are iris crypts useful in identity recognition?

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