Iris Recognition Using Feature Extraction of Box Counting Fractal Dimension

Khotimah, Chusnul; Juniati, Dwi

doi:10.1088/1742-6596/947/1/012004

Cited by 14 publications

(4 citation statements)

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“…The approach adopted by Khotimah and Juniati [21] hinged on Daugman's rubber sheet model to capture eye patterns. Feature extraction in this method was executed using box-counting, followed by feature matching facilitated by KNN and k-fold cross-validation.…”

Section: Related Workmentioning

confidence: 99%

Iris Detection for Attendance Monitoring in Educational Institutes Amidst a Pandemic: A Machine Learning Approach

Haq,

Saqlain

2023

JII

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Amid the COVID-19 pandemic, the imperative for alternative biometric attendance systems has arisen. Traditionally, fingerprint and facial recognition have been employed; however, these methods posed challenges in adherence to Standard Operational Procedures (SOPs) set during the pandemic. In response to these limitations, iris detection has been advanced as a superior alternative. This research introduces a novel machine learning approach to iris detection, tailored specifically for educational environments. Addressing the restrictions posed by COVID-19 SOPs, which permitted only 50% of student occupancy, an automated e-attendance mechanism has been proposed. The methodology comprises four distinct phases: initial registration of the student's iris, subsequent identity verification upon institutional entry, evaluation of individual attendance during examinations to assess exam eligibility, and the maintenance of a defaulter list. To validate the efficiency and accuracy of the proposed system, a series of experiments were conducted. Results indicate that the proposed system exhibits remarkable accuracy in comparison to conventional methods. Furthermore, a desktop application was developed to facilitate real-time iris detection.

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Section: Related Workmentioning

confidence: 99%

Iris Detection for Attendance Monitoring in Educational Institutes Amidst a Pandemic: A Machine Learning Approach

Haq,

Saqlain

2023

JII

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“…In [7], the authors proposed an iris recognition system based on "Fractal dimension of box-counting method". First, the iris is segmented by Hough transform and is normalized by Daugman's rubber sheet model.…”

Section: Fusion At Matching Score Levelmentioning

confidence: 99%

Dual iris authentication system using dezert smarandache theory

Ghalem¹,

Hendel²

2019

IJECE

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In this paper, a dual iris authentication using Dezert Smarandache theory is presented. The proposed method consists of three main steps: In the first one, the iris images are segmented in order to extract only half iris disc that contains relevant information and is less affected by noise. For that, a Hough transform is used. The segmented images are normalized by Daugman rubber sheet model. In the second step, the normalized images are analyzed by a bench of two 1D Log-Gabor filters to extract the texture characteristics. The encoding is realized with a phase of quantization developed by J. Daugman to generate the binary iris template. For the authentication and the similarity measurement between both binary irises templates, the hamming distances are used with a previously calculated threshold. The score fusion is applied using DSmC combination rule. The proposed method has been tested on a subset of iris database CASIA-IrisV3-Interval. The obtained results give a satisfactory performance with accuracy of 99.96%, FAR of 0%, FRR of 3.89%, EER of 2% and processing time for one iris image of 12.36 s.<em> </em>

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“…Since the model is well suited only for the process of detection and normalization, it needs to be demonstrated for the different combinations of spatial and transform domain descriptors on various iris datasets. Khotimah et al, [12] employed Hough transform and Daugman's rubber sheet model to locate the iris area and to normalize the iris data set into blocks respectively. To extract dimensional values of the iris, box counting technique is applied on normalized data.…”

Section: Related Workmentioning

confidence: 99%

Performance Analysis of Iris Recognition Using Multi Stage Wavelet Transform Decomposition and Bicubic Interpolation Technique

Harakannanavar¹,

Prashanth²,

Raja³

2019

IJEAT

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Biometric identification is highly reliable for human identification. Biometric is a field of science used for analyzing the physiological or behavioural characteristics of human. Iris features are unique, stable and can be visible from longer distances. It uses mathematical pattern-recognition techniques on video images of one or both iris of an individual's. Compared to other biometric traits, iris is more challenging and highly secured tool to identify the individual. In this paper iris recognition based on the combination of Discrete Wavelet Transform (DWT), Inverse Discrete Wavelet Transform (IDWT), Independent Component Analysis (ICA) and Binariezed Statistical Image Features (BSIF) are adopted to generate the hybrid iris features. The first level and second level DWT are employed in order to extract the more unique features of the iris images. The concept of bicubic interpolation is applied on high frequency coefficients generated by first level decomposition of DWT to produce new set of sub-bands. The approximation band generated by second level decomposition of DWT and new set of sub-bands produced by second level decomposition of DWT are applied on IDWT to reconstruct the coefficients. The ICA 5x5 filters and BSIF are adopted for selecting the appropriate images to extract the final features. Finally based on the matching score between the database image and test image the genuine and imposters are identified. Using CASIA database, training and testing of the features is performed and performance is evaluated considering different combinations of Person inside Database (PID) and Person outside Database (POD).

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Iris Recognition Using Feature Extraction of Box Counting Fractal Dimension

Cited by 14 publications

References 1 publication

Iris Detection for Attendance Monitoring in Educational Institutes Amidst a Pandemic: A Machine Learning Approach

Iris Detection for Attendance Monitoring in Educational Institutes Amidst a Pandemic: A Machine Learning Approach

Dual iris authentication system using dezert smarandache theory

Performance Analysis of Iris Recognition Using Multi Stage Wavelet Transform Decomposition and Bicubic Interpolation Technique

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