Iris image segmentation and sub-optimal images

Matey, James R.; Broussard, Randy P.; Kennell, Lauren R.

doi:10.1016/j.imavis.2009.05.006

Cited by 46 publications

(18 citation statements)

References 26 publications

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“…They are considered as the most attractive areas for biometric schemes [29][30][31][32][33][34][35]. The unimodal face and iris system processing steps include preprocessing, feature extraction and producing matching scores.…”

Section: Unimodal Biometric Systemsmentioning

confidence: 99%

Optimal Face-Iris Multimodal Fusion Scheme

Sharifi

Eskandari

2016

Symmetry

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Multimodal biometric systems are considered a way to minimize the limitations raised by single traits. This paper proposes new schemes based on score level, feature level and decision level fusion to efficiently fuse face and iris modalities. Log-Gabor transformation is applied as the feature extraction method on face and iris modalities. At each level of fusion, different schemes are proposed to improve the recognition performance and, finally, a combination of schemes at different fusion levels constructs an optimized and robust scheme. In this study, CASIA Iris Distance database is used to examine the robustness of all unimodal and multimodal schemes. In addition, Backtracking Search Algorithm (BSA), a novel population-based iterative evolutionary algorithm, is applied to improve the recognition accuracy of schemes by reducing the number of features and selecting the optimized weights for feature level and score level fusion, respectively. Experimental results on verification rates demonstrate a significant improvement of proposed fusion schemes over unimodal and multimodal fusion methods.

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Section: Unimodal Biometric Systemsmentioning

confidence: 99%

Optimal Face-Iris Multimodal Fusion Scheme

Sharifi

Eskandari

2016

Symmetry

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“…Proença et al [4] identify the critical role of segmentation and observe a strong relationship between translational segmentation inaccuracies and recognition error rates. Matey et al [5]. assess the effect of resolution, wavelength, occlusion and gaze as the most important factors for incorrect segmentation and give a survey of segmentation algorithms.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Iris Matching Using Levenshtein Distance with Alignment Constraints

Uhl

Wild

2010

Advances in Visual Computing

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Abstract. Iris recognition from surveillance-type imagery is an active research topic in biometrics. However, iris identification in unconstrained conditions raises many proplems related to localization and alignment, and typically leads to degraded recognition rates. While development has mainly focused on more robust preprocessing, this work highlights the possibility to account for distortions at matching stage. We propose a constrained version of the Levenshtein Distance (LD) for matching of binary iris-codes as an alternative to the widely accepted Hamming Distance (HD) to account for iris texture distortions by e.g. segmentation errors or pupil dilation. Constrained LD will be shown to outperform HD-based matching on CASIA (third version) and ICE (2005 edition) datasets. By introducing LD alignment constraints, the matching problem can be solved in O(n · s) time and O(n + s) space with n and s being the number of bits and shifts, respectively.

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“…Automatic detection of circles in images can be found in a wide set of applications, ranging from analysis of football games [1], banknote printing [2], exploration of lunar craters [3] to iris detection [4]. Common difficulties for all these algorithms seems to be the lack of precision when subjected to background noise, shading and occlusion of the circular objects.…”

Section: Methodsmentioning

confidence: 99%

Detecting halftone dots for offset print quality assessment using soft computing

Lundström

Verikas

2010

International Conference on Fuzzy Systems

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Abstract-Nowadays in printing industry most of information processing steps are highly automated, except the last one-print quality assessment and control. We present a way to assess one important aspect of print quality, namely the distortion of halftone dots printed colour pictures are made of. The problem is formulated as assessing the distortion of circles detected in microscale images of halftone dot areas. In this paper several known circle detection techniques are explored in terms of accuracy and robustness. We also present a new circle detection technique based on the fuzzy Hough transform (FHT) extended with k-means clustering for detecting positions of accumulator peaks and with an optional fine-tuning step implemented through unsupervised learning. Prior knowledge about the approximate positions and radii of the circles is utilized in the algorithm. Compared to FHT the proposed technique is shown to increase the estimation accuracy of the position and size of detected circles. The techniques are investigated using synthetic and natural images.

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Iris image segmentation and sub-optimal images

Cited by 46 publications

References 26 publications

Optimal Face-Iris Multimodal Fusion Scheme

Optimal Face-Iris Multimodal Fusion Scheme

Enhancing Iris Matching Using Levenshtein Distance with Alignment Constraints

Detecting halftone dots for offset print quality assessment using soft computing

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