Journal Article•
Iris recognition based on multialgorithmic fusion
01 Dec 2007-WSEAS Transactions on Information Science and Applications archive (World Scientific and Engineering Academy and Society (WSEAS))-Vol. 4, Iss: 12, pp 1415-1421
TL;DR: The experimental results on CASIA and UBIRIS iris image databases show that the proposed multialgorithmic fusion method can bring obvious performance improvement compared with any single algorithm, and the results also demonstrate that the fusion rule based on SVM can achieve better performance than conventional 1 fusion rules.
Abstract: Fusion of multiple algorithms for biometric verification performance improvement has received considerable attention. This paper proposes an iris recognition method based on multialgorithmic fusion. The proposed method combines the phase information based algorithm and zero-crossing representation based algorithm at the matching score level. The fusion rule based on support vector machine (SVM) is applied to generate a fused score which is used to make the fial decision. The experimental results on CASIA and UBIRIS iris image databases show that the proposed multialgorithmic fusion method can bring obvious performance improvement compared with any single algorithm, and the results also demonstrate that the fusion rule based on SVM can achieve better performance than conventional 1 fusion rules.
Citations
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TL;DR: A method that combined multiscale sparse representation of local Radon transform was proposed to down sample a normalized iris into different lengths of scales and different orientations of angles to form an iris feature vector and showed that the proposed method performed better than existing methods when dealing with iris images captured at different distances.
35 citations
Cites background or methods from "Iris recognition based on multialgo..."
...[9] combined a phase based structure information algorithm (2D Gabor filter) and zerocrossing based algorithm (1D wavelet transform) to extract iris features in cooperative and non-cooperative databases....
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...[9] provided the lowest DI values with values of 3....
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...[9] was the least accurate as it achieved an accuracy rate of 85% for images captured at 4 m and only 81% for images taken at 5 ms....
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01 Jan 2012
TL;DR: With respect to the design goals, biometric cryptosystems and cancelable biometrics offer significant advantages to enhance the privacy and security of biometric systems, providing reliable biometric authentication at a high security level.
Abstract: Biometric cryptosystems and cancelable biometrics offer several advantages over generic biometric systems. The most important advantages are summarized in Table 15.1. These major advantages over conventional biometric systems call for several applications. In order to underline the potential of both technologies two essential use cases are discussed in detail. With respect to the design goals, biometric cryptosystems and cancelable biometrics offer significant advantages to enhance the privacy and security of biometric systems, providing reliable biometric authentication at a high security level. Techniques which provide provable security/privacy, while achieving practical recognition rates have remained elusive (even on small datasets).
21 citations
01 Jan 2012
TL;DR: Cancelable biometrics consist of intentional, repeatable distortions ofBiometric signals based on transforms which provide a comparison of biometric templates in the transformed domain.
Abstract: Cancelable biometrics consist of intentional, repeatable distortions of biometric signals based on transforms which provide a comparison of biometric templates in the transformed domain [418].
16 citations
22 Apr 2013
TL;DR: The experimental results show 7% relative improvement on average with regard to equal error rate of the false acceptance rate and false rejection rate in verification scenarios, and also show 20% reduction of the number of candidates to be checked under 1% misdetection rate on average in screening tasks.
Abstract: This paper describes a method of gait recognition using multiple gait features in conjunction with score-level fusion techniques. More specifically, we focus on the state-of-the-art period-based gait features such as a gait energy image, a frequency-domain feature, a gait entropy image, a chrono-gait image, and a gait flow image. In addition, we employ various types of the score-level fusion approaches including not only conventional transformation-based approaches (e.g., sum-rule and min-rule) but also classification-based approaches (e.g., support vector machine) and density-based approaches (e.g., Gaussian mixture model, kernel density estimation, linear logistic regression). In experiments, the large-population gait database with 3,249 subjects was used to measure the performance improvement in a statistically reliable way. The experimental results show 7% relative improvement on average with regard to equal error rate of the false acceptance rate and false rejection rate in verification scenarios, and also show 20% reduction of the number of candidates to be checked under 1% misdetection rate on average in screening tasks.
13 citations
Additional excerpts
..., fingerprint [62], [10], iris [59], [73], and face [15], [11], [55])....
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01 Jan 2012
TL;DR: Traditional iris processing following Daugman’s approach extracts binary features after mapping the textural area between inner pupillary and outer limbic boundary into a doubly dimensionless representation.
Abstract: Traditional iris processing following Daugman’s approach [116] extracts binary features after mapping the textural area between inner pupillary and outer limbic boundary into a doubly dimensionless representation.
12 citations
References
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Book•
01 Jan 1995TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Abstract: Setting of the learning problem consistency of learning processes bounds on the rate of convergence of learning processes controlling the generalization ability of learning processes constructing learning algorithms what is important in learning theory?.
40,147 citations
Book•
11 Aug 2011TL;DR: The authors describe an algorithm that reconstructs a close approximation of 1-D and 2-D signals from their multiscale edges and shows that the evolution of wavelet local maxima across scales characterize the local shape of irregular structures.
Abstract: A multiscale Canny edge detection is equivalent to finding the local maxima of a wavelet transform. The authors study the properties of multiscale edges through the wavelet theory. For pattern recognition, one often needs to discriminate different types of edges. They show that the evolution of wavelet local maxima across scales characterize the local shape of irregular structures. Numerical descriptors of edge types are derived. The completeness of a multiscale edge representation is also studied. The authors describe an algorithm that reconstructs a close approximation of 1-D and 2-D signals from their multiscale edges. For images, the reconstruction errors are below visual sensitivity. As an application, a compact image coding algorithm that selects important edges and compresses the image data by factors over 30 has been implemented. >
3,187 citations
"Iris recognition based on multialgo..." refers methods in this paper
...3 6 2 {( ( )) } j S j Z I ≤ ≤ The dyadic wavelet used in this work is the quadratic spline of compact support defined in [11]....
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TL;DR: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests.
Abstract: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests. The recognition principle is the failure of a test of statistical independence on iris phase structure encoded by multi-scale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 b/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. The high confidence levels are important because they allow very large databases to be searched exhaustively (one-to-many "identification mode") without making false matches, despite so many chances. Biometrics that lack this property can only survive one-to-one ("verification") or few comparisons. The paper explains the iris recognition algorithms and presents results of 9.1 million comparisons among eye images from trials in Britain, the USA, Japan, and Korea.
2,829 citations
10 Dec 2002
TL;DR: Algorithms developed by the author for recognizing persons by their iris patterns have now been tested in many field and laboratory trials, producing no false matches in several million comparison tests.
Abstract: The principle that underlies the recognition of persons by their iris patterns is the failure of a test of statistical independence on texture phase structure as encoded by multiscale quadrature wavelets. The combinatorial complexity of this phase information across different persons spans about 249 degrees of freedom and generates a discrimination entropy of about 3.2 bits/mm/sup 2/ over the iris, enabling real-time decisions about personal identity with extremely high confidence. Algorithms first described by the author in 1993 have now been tested in several independent field trials and are becoming widely licensed. This presentation reviews how the algorithms work and presents the results of 9.1 million comparisons among different eye images acquired in trials in Britain, the USA, Korea, and Japan.
2,437 citations
"Iris recognition based on multialgo..." refers methods in this paper
...Structure Information The iris recognition algorithm based on phase structure information was proposed by Daugman[2]....
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...2 Steps involved in iris preprocessing Feature encoding Complex 2D Gabor filters ( , ) g x y are employed to extract the phase information of the iris texture[2]....
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...References: [1] SANJAY R. GANORKAR, Iris Recognition: An Emerging Biometric Technology, Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation, Corfu Island, Greece, February 16-19, 2007, pp. 91-96 [2] J. Daugman, How Iris Recognition Works, IEEE Transaction on Circuits and Systems for ISSN: 1790-0832 1420 Issue 12, Volume 4, December 2007 Video Technology, Vol.14, No.1, 2004, pp. 21- 30....
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...1 Iris Recognition Based on Phase Structure Information The iris recognition algorithm based on phase structure information was proposed by Daugman[2]....
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...Daugman used multi-scale 2D Gabor filters to extract texture phase structure information of the iris and Hamming distance for classification[2]....
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TL;DR: This paper addresses the problem of information fusion in biometric verification systems by combining information at the matching score level by combining three biometric modalities (face, fingerprint and hand geometry).
1,611 citations
"Iris recognition based on multialgo..." refers methods in this paper
...To improve the identification performance, multibiometric fusion techniques are applied in some literatures[6][7]....
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