Evaluation of the Reliability of Iris Recognition Biometric Authentication Systems

Alqahtani, Abdulrahman

doi:10.1109/csci.2016.0152

Cited by 8 publications

(2 citation statements)

References 7 publications

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“…[ 2 , 3 , 4 ]. Compared with other mainstream biometric approaches, including face recognition [ 5 ], palmprint recognition [ 6 ], and fingerprint recognition [ 7 ], iris recognition is safer and more sanitary because of its characteristics of being non-contact and having less exposure [ 8 ]. The merits of iris recognition have prompted increasing efforts for investigating more accurate and efficient iris feature extraction algorithms under various conditions [ 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network

Liu

Zhou

Lan

et al. 2021

Sensors

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Recently, deep learning approaches, especially convolutional neural networks (CNNs), have attracted extensive attention in iris recognition. Though CNN-based approaches realize automatic feature extraction and achieve outstanding performance, they usually require more training samples and higher computational complexity than the classic methods. This work focuses on training a novel condensed 2-channel (2-ch) CNN with few training samples for efficient and accurate iris identification and verification. A multi-branch CNN with three well-designed online augmentation schemes and radial attention layers is first proposed as a high-performance basic iris classifier. Then, both branch pruning and channel pruning are achieved by analyzing the weight distribution of the model. Finally, fast finetuning is optionally applied, which can significantly improve the performance of the pruned CNN while alleviating the computational burden. In addition, we further investigate the encoding ability of 2-ch CNN and propose an efficient iris recognition scheme suitable for large database application scenarios. Moreover, the gradient-based analysis results indicate that the proposed algorithm is robust to various image contaminations. We comprehensively evaluated our algorithm on three publicly available iris databases for which the results proved satisfactory for real-time iris recognition.

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Section: Introductionmentioning

confidence: 99%

An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network

Liu

Zhou

Lan

et al. 2021

Sensors

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“…Physiological biometrics include fingerprint recognition [ 2 ], finger vein pattern recognition [ 3 ], face recognition [ 4 ], iris recognition [ 5 ], and palmprint recognition [ 6 ]. Iris recognition has been proven as innovative reliable biometric widely used in security, authentication, and identification systems [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

Arsalan

Naqvi

Kim

et al. 2018

Sensors

100

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The recent advancements in computer vision have opened new horizons for deploying biometric recognition algorithms in mobile and handheld devices. Similarly, iris recognition is now much needed in unconstraint scenarios with accuracy. These environments make the acquired iris image exhibit occlusion, low resolution, blur, unusual glint, ghost effect, and off-angles. The prevailing segmentation algorithms cannot cope with these constraints. In addition, owing to the unavailability of near-infrared (NIR) light, iris recognition in visible light environment makes the iris segmentation challenging with the noise of visible light. Deep learning with convolutional neural networks (CNN) has brought a considerable breakthrough in various applications. To address the iris segmentation issues in challenging situations by visible light and near-infrared light camera sensors, this paper proposes a densely connected fully convolutional network (IrisDenseNet), which can determine the true iris boundary even with inferior-quality images by using better information gradient flow between the dense blocks. In the experiments conducted, five datasets of visible light and NIR environments were used. For visible light environment, noisy iris challenge evaluation part-II (NICE-II selected from UBIRIS.v2 database) and mobile iris challenge evaluation (MICHE-I) datasets were used. For NIR environment, the institute of automation, Chinese academy of sciences (CASIA) v4.0 interval, CASIA v4.0 distance, and IIT Delhi v1.0 iris datasets were used. Experimental results showed the optimal segmentation of the proposed IrisDenseNet and its excellent performance over existing algorithms for all five datasets.

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Reliable Biometric Authentication with Privacy Protection

Sharma

Dhall

Nayak

et al. 2022

Advances in Communication, Devices and Networking

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Evaluation of the Reliability of Iris Recognition Biometric Authentication Systems

Cited by 8 publications

References 7 publications

An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network

An Efficient and Accurate Iris Recognition Algorithm Based on a Novel Condensed 2-ch Deep Convolutional Neural Network

IrisDenseNet: Robust Iris Segmentation Using Densely Connected Fully Convolutional Networks in the Images by Visible Light and Near-Infrared Light Camera Sensors

Reliable Biometric Authentication with Privacy Protection

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