Iris recognition using partial sum of second order Taylor series expansion

Shekar, B. H.; Bhat, Sharada S.

doi:10.1145/3009977.3010065

Cited by 10 publications

(2 citation statements)

References 30 publications

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“…For iris segmentation we have adopted the method proposed by (Proenca, 2010). In order to extract the iris features, we have adopted the filters, DCT (Monro et al, 2007), Log-Gabor (Masek et al, 2003), Riesz (Shekar and Bhat, 2015) and TSE (Shekar and Bhat, 2016). Daugman's rubber sheet model and phase encoding techniques are used for unwrapping the iris and encoding the features respectively.…”

Section: Empirical Illustrationmentioning

confidence: 99%

Reliability Index for Iris Code Matching in Iris Recognition Systems

Shekar

Bhat²,

Maysuradze

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> Iris code matching is an important stage of iris biometric systems which compares the input iris code with stored patterns of enrolled iris codes and classifies the code into one of classes so that, the claim is accepted or rejected. Several classifier based approaches are proposed by the researchers to improve the recognition accuracy. In this paper, we discuss the factors affecting an iris classifier’s performance and we propose a reliability index for iris matching techniques to quantitatively measure the extent of system reliability, based on false acceptance rate and false rejection rates using Monte Carlo Simulation. Experiments are carried out on benchmark databases such as, IITD, MMU v-2, CASIA v-4 Distance and UBIRIS v.2.</p>

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Section: Empirical Illustrationmentioning

confidence: 99%

Reliability Index for Iris Code Matching in Iris Recognition Systems

Shekar

Bhat²,

Maysuradze

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…We have used Log-Gabor filter given by Masek (Masek and Kovesi, 2003), Riesz filter (Shekar and Bhat, 2015) and Taylor series expansion (TSE) filter (Shekar and Bhat, 2016) and the combinations of these filters and have compared the results. A brief introduction of these filters is given below.…”

Section: Iris Feature Extraction Filtersmentioning

confidence: 99%

Multi-Patches Iris Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

Shekar

Bhat²

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Locating the boundary parameters of pupil and iris and segmenting the noise free iris portion are the most challenging phases of an automated iris recognition system. In this paper, we have presented person authentication frame work which uses particle swarm optimization (PSO) to locate iris region and circular hough transform (CHT) to device the boundary parameters. To undermine the effect of the noise presented in the segmented iris region we have divided the candidate region into N patches and used Fuzzy c-means clustering (FCM) to classify the patches into best iris region and not so best iris region (noisy region) based on the probability density function of each patch. Weighted mean Hammimng distance is adopted to find the dissimilarity score between the two candidate irises. We have used Log-Gabor, Riesz and Taylor's series expansion (TSE) filters and combinations of these three for iris feature extraction. To justify the feasibility of the proposed method, we experimented on the three publicly available data sets IITD, MMU v-2 and CASIA v-4 distance.

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Edge Detection in Gray Scale Images Using Partial Sum of Second Order Taylor Series Expansion

Shekar,

Bhat

2024

Lecture Notes in Computer Science

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Iris recognition using partial sum of second order Taylor series expansion

Cited by 10 publications

References 30 publications

Reliability Index for Iris Code Matching in Iris Recognition Systems

Reliability Index for Iris Code Matching in Iris Recognition Systems

Multi-Patches Iris Based Person Authentication System Using Particle Swarm Optimization and Fuzzy C-Means Clustering

Edge Detection in Gray Scale Images Using Partial Sum of Second Order Taylor Series Expansion

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