Iris recognition based on sparse representation and k-nearest subspace with genetic algorithm

Bhateja, Ashok K.; Sharma, Shikhar; Chaudhury, Santanu; Agrawal, Nitin

doi:10.1016/j.patrec.2015.12.009

Cited by 38 publications

(9 citation statements)

References 12 publications

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“…Another method proposed by Ashok et al [16] makes use of the power of three classification types based on k-nearest distance, sparse display, and genetic algorithm. The experiments show that the false error rate (FER) of this method approximates is very low.…”

Section: Related Workmentioning

confidence: 99%

“…The result of applying this transform to image signal consists of two complementary signals, namely, approximation and details. The former is a representation of low-frequency parts of image and the latter is a representation of highfrequency parts of image [16]. Therefore, repetitive application of DWT decomposes the image signal into different sub-bands so that the lower-frequency sub-bands have finer frequency resolution and coarser time resolution compared to the higher-frequency sub-bands.…”

Section: Wavelet Transformmentioning

confidence: 99%

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Improving identification performance in iris recognition systems through combined feature extraction based on binary genetics

Abbasi

2019

SN Appl. Sci.

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The process of iris recognition is composed of several stages. The most important stage is feature extraction. The majority of systems use a single method of feature extraction. We used a binary genetic algorithm with a new fitness criterion to find an efficient combination of feature extraction methods with the aim of improving the performance of iris recognition systems. The proposed method makes use of a large number of filters and transforms which are widely used to extract iris features. The best combination of these techniques is found over iterations of the proposed algorithm. The final combination, which is argued to be the most efficient method of feature extraction, consists of a number of wavelet transforms, Gabor filter, and Fourier transform. ROC graphs are used to show the experimental superiority of the performance of this method over methods that use a single filter. According to our findings, the proposed method shows better performance in most situations. An FAR value of 0 and an FRR value of 0.092 was obtained by this method.

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Section: Related Workmentioning

confidence: 99%

Section: Wavelet Transformmentioning

confidence: 99%

Improving identification performance in iris recognition systems through combined feature extraction based on binary genetics

Abbasi

2019

SN Appl. Sci.

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“…The most important characteristics of the iris do not change the texture of the iris through a person life [30,31]. This stability of iris features over a long time, leading to guarantees the long period of validity of the data and it does not need to update; in addition, iris characteristics are well protected from the environment [32][33][34]. This advantage allows iris identification as the most accurate and reliable biometric identification [35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Efficient Biometric Iris Recognition Based on Iris Localization Approach

Al-Ani

2019

UHD J SCI TECH

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Biometric recognition is an emerging technology that has attracted more attention in recent years. This issue uses physiological and behavioral characteristics to identify an individual. The iris recognition approaches are among the most accurate biometric technologies with immense potential for use in global security applications. The purpose of this research is to implement an efficient approach to treat the diseased eye images in order to verify the second iris examination for the same person by inserting an improvement step to the iris recognition system. This step makes a correction of boundary around pupil and removes fraction regions. This approach demonstrates its ability to detect the inner limit of the iris. The obtained results shows that 90% success in the detection diseased eye images, which makes the iris recognition system more accurate and safe.

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“…This is in contrast to other unsupervised techniques that are traditionally used, such as PCA, LPP and random projections. SRC is performed using our adaptive reduced‐set matching pursuit (ARMP) algorithm [13]. Other frameworks that employ SRC typically perform CS reconstruction using the computationally expensive ℓ 1 minimisation such as those in [9, 14]. In contrast, ARMP is an efficient CS reconstruction algorithm that significantly improves the speed of SRC while it maintains the same accuracy.…”

Section: Introductionmentioning

confidence: 99%

Efficient high‐speed framework for sparse representation‐based iris recognition

2021

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While various frameworks for iris recognition have been proposed, most lack efficiency and high speed. A new framework for iris recognition is presented that is both efficient and fast. Feature extraction is performed by extracting Gabor features and then applying supervised locality‐preserving projections with heat kernel weights, which improves the recognition rate in comparison with the results from unsupervised dimensionality reduction techniques such as principal component analysis, locality‐preserving projections, and random projections. Afterwards, a classification is performed using the recently proposed sparse representation‐based classification (SRC). To considerably improve classification performance, SRC is proposed, using a greedy compressed‐sensing recovery algorithm, as opposed to employing the traditional computationally expensive ℓ1 minimisation. The proposed framework achieves a recognition rate of about 99.5% using two iris databases, with a significant improvement in speed over related frameworks.

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Iris recognition based on sparse representation and k-nearest subspace with genetic algorithm

Cited by 38 publications

References 12 publications

Improving identification performance in iris recognition systems through combined feature extraction based on binary genetics

Improving identification performance in iris recognition systems through combined feature extraction based on binary genetics

Efficient Biometric Iris Recognition Based on Iris Localization Approach

Efficient high‐speed framework for sparse representation‐based iris recognition

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