Iris Biometric Technique for Person Authentication Based on Fusion of Radon and 2D Multi-Wavelet Transform

Rajput, Minakshi R.; Sable, Ganesh S.; Gite, Hanumant R.

doi:10.1109/icacct.2018.8529514

Cited by 2 publications

(3 citation statements)

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“…Finally, multivariate wavelet frames are successively used in applications related to signal processing and numerical solution of PDEs (for example, in [5] 2D multiwavelet transform is used in the iris biometric technique for person authentication, in [1] 2D multiwavelets are used for dimensionality reduction and features extraction in the algorithm of face recognition, in [21] 2D multiwavelets are applied to variational image denoising and in [4] 2D multiwavelets are used for the compression, analysis and assembly of hydrodynamic model data within Godunov-type solvers based on second-order discontinuous Galerkin methods). Mostly, 2D multiwavelets in the above-mentioned applications and in the literature are separable, i.e., they are obtained as a tensor product of 1D multiwavelets.…”

Section: Further Details and Discussionmentioning

confidence: 99%

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Multivariate Symmetric Interpolating Dual Multiwavelet Frames

Krivoshein

2022

Symmetry

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The construction of symmetric multiwavelets in the multivariate case with useful in applications properties is a challenging task, mainly due to the complexity of the matrix extension problem. Nevertheless, for the interpolating case, a general technique can be developed. For an appropriate pair of symmetry group H and matrix dilation M and for a given H-symmetric interpolating refinable matrix mask, a method for the construction of H-symmetric dual refinable matrix masks with a preassigned order of sum rule is suggested. Wavelet matrix masks are constructed using a certain explicit matrix extension algorithm, and their symmetry properties are studied via its polyphase components. The resulting multiwavelet systems form dual multiwavelet frames, where wavelet functions have symmetry properties, preassigned order of vanishing moments and preassigned order of the balancing property. Several examples are presented.

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Section: Further Details and Discussionmentioning

confidence: 99%

“…Wavelet systems are widely used in applied mathematics for signal analysis and synthesis, compression tasks, data preprocessing and feature extraction for neural networks and so on in a large number of applications (see, for example, [1][2][3][4][5][6]). The symmetry property is among the most desirable features for wavelets.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Symmetric Interpolating Dual Multiwavelet Frames

Krivoshein

2022

Symmetry

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“…The CASIA V3 and Indian Institute of Technology Delhi (IITD) databases were used. Rajput et al [6] proposed a fusion radon multiwavelet transform-based iris feature extraction technique. The circular Hough transform (CHT) technique was used to segment the iris.…”

Section: Literature Reviewmentioning

confidence: 99%

An effective model for the iris regional characteristics and classification using deep learning alex network

Thiyaneswaran¹,

Sengottaiyan²,

Kulandairaj

et al. 2022

IET Image Processing

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Iris biometrics is one of the fastest-growing technologies, and it has received a lot of attention from the community. Iris-biometric-based human recognition does not require contact with the human body. Iris is a combination of crypts, wolflin nodules, concentrated furrows, and pigment spots. The existing methods feed the eye image into deep learning network which result in improper iris features and certainly reduce the accuracy. This research study proposes a model to feed preprocessed accurate iris boundary into Alexnet deep learning neural network-based system for classification. The pupil centre and boundary are initially recorded and identified from the given eye images. The iris boundary and the centre are then compared for the identification using the reference pupil centre and boundary. The iris portion, exclusive feature of the pupil area is segmented using the parameters of multiple left-right point (MLRP) algorithms. The Alexnet deep learning multilayer networks 23, 24, and 25 are replaced according to the segmented iris classes. The remaining Alexnet layers are trained using the square gradient decay factor (GDF) in accordance with the iris features. The trained Alexnet iris is validated using suitable classes. The proposed system classifies the iris with an accuracy of 99.1%. The sensitivity, specificity, and F1-score of the proposed system are 99.68%, 98.36%, and 0.995. The experimental results show that the proposed model has advantages over current models.

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Iris Biometric Technique for Person Authentication Based on Fusion of Radon and 2D Multi-Wavelet Transform

Cited by 2 publications

References 5 publications

Multivariate Symmetric Interpolating Dual Multiwavelet Frames

Multivariate Symmetric Interpolating Dual Multiwavelet Frames

An effective model for the iris regional characteristics and classification using deep learning alex network

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