SVD-EBP Algorithm for Iris Pattern Recognition

Patil, Babasaheb G.; Subbaraman, Shaila

doi:10.48550/arxiv.1204.2062

Cited by 1 publication

(6 citation statements)

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“…These features are applied as an input to the stage of iris classification of BP neural network. Let semi discrete decomposition of iris image (Is) is defined as in the (12).…”

Section: Features Extractionmentioning

confidence: 99%

“…𝐼𝑠 ≈ 𝐼𝑠 𝑧 = 𝑋 𝑧 𝐷 𝑧 𝑌 𝑧 𝑇 (12) Where R denotes the rank of the original matrix Is (iris image), z is reduced singular decomposition of Is. z(0<z<R).…”

Section: Features Extractionmentioning

confidence: 99%

“…The recognition rate of NN system based on SDD is 95%. The recognition rate of this study was compared with previous work [12] in order to show the effect of SVD method and the recent version of SVD which is SDD to improve the recognition rate. Patil and Subbaraman [12] used SVD to extract features.…”

Section: Previous Workmentioning

confidence: 99%

“…The recognition rate of this study was compared with previous work [12] in order to show the effect of SVD method and the recent version of SVD which is SDD to improve the recognition rate. Patil and Subbaraman [12] used SVD to extract features. The comparison was made in term of classification accuracy based on different number of SVD and SDD dimensions and several number of classes as shown in Table 3.…”

Section: Previous Workmentioning

confidence: 99%

“…Recent advances in numerical linear algebra have influenced image feature extraction techniques such as singular value decomposition (SVD) and semi-discrete decomposition (SDD). SVD has been used in [11], [12] to extract iris characteristics. While in [13] SDD was used to present a reduced number of feature words.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

New algorithm for localization of iris recognition using deep learning neural networks

Ali

Jaber

Kadhim

2022

IJEECS

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Iris recognition is the most reliable and accurate method for eye identification. A novel strategy for localizing iris printing is proposed in this paper. The median filter and histogram were used for this purpose. To extract iris features from iris photographs, an algebraic method known as semi-discrete matrix decomposition (SDD) is used. For classification, neural network (NN) is used to extract the SDD feature. This study also included the setup of convolution neural network (CNN), a convolution neural network that does not require feature extraction, as well as a comparison of the two types of classifiers is made. Iris images are obtained from the Chinese Academy of Sciences Institute of Automation dataset (CASIA Iris-V1), a common database used for the iris recognition system. The proposed algorithm is straightforward, simple, efficient, and fast. The experimental results showed that the proposed algorithm achieved high classification accuracy of approximately 95.5% and 95% for CNN and NN based on SDD features respectively. The proposed algorithms outperformed literature works and required less time for determining the location of iris region.

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“…These features are applied as an input to the stage of iris classification of BP neural network. Let semi discrete decomposition of iris image (Is) is defined as in the (12).…”

Section: Features Extractionmentioning

confidence: 99%

“…𝐼𝑠 ≈ 𝐼𝑠 𝑧 = 𝑋 𝑧 𝐷 𝑧 𝑌 𝑧 𝑇 (12) Where R denotes the rank of the original matrix Is (iris image), z is reduced singular decomposition of Is. z(0<z<R).…”

Section: Features Extractionmentioning

confidence: 99%