Human iris as a biometric for identity verification

Matin, Abdul; Mahmud, Firoz; Zuhori, Syed Tauhid; Sen, Barshon

doi:10.1109/icecte.2016.7879610

Cited by 14 publications

(13 citation statements)

References 13 publications

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“…The method proposed is tested with two databases such as Chinese Academy of Sciences Institute of Automation (CASIA)-v4-Interval and IITD. In [19] a segmentation technique of iris using CHT is presented. The approach consists on changing the segmentation used in wavelet packet decomposition from integro-differential operator to Hough transform.…”

Section: Related Workmentioning

confidence: 99%

“…A low EER value indicates a high accuracy of the biometric system. [19] 99.17% -Salve and Narote [22] 95.9% -Okokpujie et al [20] 93.06% 90.5% Vyas et al [18] 92.92% 98.97% Meenakshi [23] 98.85% 99.8% Sardar et al [21] 97.12% 97.19% Proposed method (Gabor filter+steerable pyramid) 99.99% 99.98% [18] 98.97% Ahmed et al [13] 99.99% Proposed method (Gabor filter+steerable pyramid) 99.99%…”

Section: Equal Error Ratementioning

confidence: 99%

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Fusion of Gabor filter and steerable pyramid to improve iris recognition system

Radouane

Zouggari

Amraoui

et al. 2022

IJ-AI

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<span>Iris recognition system is a technique of identifying people using their distinctive features. Generally, this technique is used in security, because it offers a good reliability. Different researchers have proposed new methods for iris recognition system to increase its effectiveness. In this paper, we propose a new method for iris recognition based on Gabor filter and steerable pyramid decomposition. It’s an efficient and accurate linear multi-scale, multi-orientation image decomposition to capture texture details of an image. At first, the iris image is segmented, normalized and decomposed by Gabor filter and steerable pyramid method. Multiple sub-band are generated by applying steerable pyramid on the input image. High frequency sub-band is ignored to eliminate noise and increase the accuracy. The method was validated using CASIA-v4 (Chinese Academy of Sciences Institute of Automation), IITD (</span><span>Indian Institute of Technology Delhi) and UPOL (University of Phoenix Online) databases. The performance of the proposed method is better than the most methods in the literature. The proposed algorithm provides accuracy of 99.99%. False acceptance rate (FAR), equal error rate (EER) and genuine acceptance rate (GAR) have also been improved.</span>

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Section: Related Workmentioning

confidence: 99%

Section: Equal Error Ratementioning

confidence: 99%

Fusion of Gabor filter and steerable pyramid to improve iris recognition system

Radouane

Zouggari

Amraoui

et al. 2022

IJ-AI

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“…Amongst all biometric features i.e. face, iris, palm, retina, hand and palm geometry, signature, and voice, human retina is the best attribute to consider [3] for identification and verification; as it ensures unique vascular pattern resulting in accurate identification with the least probability of spoofing as it's embedded inside a body organ [4] and hardly changes over time [5] . Templates used for retina recognition and matching are quite small in size which makes the recognition and matching procedure quick [6] .…”

Section: Data Descriptionmentioning

confidence: 99%

RIDB: A Dataset of fundus images for retina based person identification

et al. 2020

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The paper describes a dataset, entitled Retina Identification Database (RIDB). The stated dataset contains Retinal fundus images acquired using Fundus imaging camera TOPCON-TRC 50 EX. The abovementioned dataset holds a significant position in retinal recognition and identification. Retinal recognition is considered as one of the reliable biometric recognition features. Biometric recognition has become an integral part of any organization's security department. Before biometrics, the information was secured through passwords, pin keys, etc. However, the fear of decryption and hacking retained. Biometric verification includes behavioural (voice, signature, gait), morphological (Fingerprint, face, palm print, retina) and biological (Odour, saliva, DNA) features [1] . Amongst all of them, retina based identification is considered as the spoof proof and most accurate identification system. Since the retina is embedded inside the eye thus is least affected by the outer environment and retain in its original state. Moreover, the vascular pattern in the retina is unique and remains unchanged during the entire life span. The data presented in the paper is composed of 100 retinal images of 20 individuals (5 images were captured from each patient). The dataset is supported by research work [2] and [7] . These research papers proposed retinal recognition algorithms for biometric verification and identification. The proposed method utilized both vascular and non-vascular features for identification and yields recognition rates of 100 % and 92.5% respectively.

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“…The security system by utilizing biometric principles, such as iris recognition, is a computational method using biological aspects by displaying the unique characteristics possessed by humans [1]. Scans that utilize these biometric principles will use biometric data to identify individuals based on measurements of their physiological characteristics [2]. Physiological characteristics are able to control and protect sensitive data contained in information systems.…”

Section: Introductionmentioning

confidence: 99%

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

Saleh

Laia

Gowasa

et al. 2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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One of the neural network algorithms that can be used in iris recognition is self-organizing map (SOM). This algorithm has a weakness in determining the initial weight of the network, which is generally carried out randomly, which can result in a decrease in accuracy when an incorrect determination is made. The solution that is often used is to apply a hybrid process in determining the initial weight of the SOM network. This study takes an approach using the cosine similarity equation to determine the initial weight of the network SOM in order to increase recognition accuracy. In addition, the localization process needs to be carried out to limit the area of the iris image being studied so that it is easy for the recognition process to be carried out. The method proposed in this study for iris recognition, namely hybrid SOM and Daugman’s algorithm, has been tested on several people by capturing the iris of the eye using a digital camera. The captured eyes have been localized first using the Daugman’s algorithm, and then the image features were extracted using the GLCM and LBP methods. In the final stage of the study, an iris recognition comparison test was performed, and the results obtained an accuracy of 85.50% using the proposed method and an accuracy of 73.50% without performing a hybrid process on the SOM network.

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Human iris as a biometric for identity verification

Cited by 14 publications

References 13 publications

Fusion of Gabor filter and steerable pyramid to improve iris recognition system

Fusion of Gabor filter and steerable pyramid to improve iris recognition system

RIDB: A Dataset of fundus images for retina based person identification

Iris Recognition Using Hybrid Self-Organizing Map Classifier and Daugman’s Algorithm

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