A comprehensive review on iris image-based biometric system

Winston, J. Jenkin; Hemanth, D. Jude

doi:10.1007/s00500-018-3497-y

Cited by 35 publications

(29 citation statements)

References 143 publications

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“…As one of the most secure and reliable biometric identification techniques, iris recognition has been widely used in banking, border security control, mobile phones, etc. [ 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 ].…”

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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“…Due to the uniqueness, noninvasiveness, stability, and natural anticounterfeiting of the iris, the iris has higher accuracy than other biometrics [1]. Therefore, iris recognition technology is one of the most popular biometric recognition technologies.…”

Section: Introductionmentioning

confidence: 99%

Iris Location Algorithm Based on Union‐Find‐Set and Block Search

Huang

Zhang²,

Duan

2021

Journal of Sensors

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In view of the problem of unstable recognition effect and low robustness of a traditional iris location algorithm, an iris location algorithm based on union-find-set and block search is proposed. Firstly, the inner circle of the iris is roughly positioned by the method of retrieval, and then, the Hough transform is used to accurately locate the pupil. After that, the convolution operation is used to roughly locate the outer circle, and then, the original image is partitioned to search. And the grayscale change in the gray histogram of the screenshot is observed to accurately locate the outer circle. The obtained iris and the iris obtained by the traditional localization algorithm are processed by the same iris recognition algorithm. The results show that the proposed image is more effective in image recognition and has good robustness.

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“…In India, the iris recognition has been used to build an iris identity database nationwide. In civil fields, the access control and attendance system based on iris recognition technology have been applied preliminary [3][4] . In information society, the importance of information security is self-evident [5] .…”

Section: Introductionmentioning

confidence: 99%

Generative Information Hiding of Iris Feature Data Based on Gaussian Fuzzy Algorithm

ShuChen

Jadoon

Rehman

et al. 2021

Preprint

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The existing methods used for hiding the iris feature data were time-consuming for iris feature extraction. Meanwhile, the information security after hiding was also low, leading to low efficiency and security of information hiding. Therefore, a method of hiding iris features data generative information based on a Gaussian fuzzy algorithm was proposed. In the preprocessing stage of the image, the weighted average method was adopted for the gray-level transformation of the iris image, and the Gaussian fuzzy algorithm was used to smooth the image. In addition, the Laplacian convolution kernel was used to sharpen the image. The iris regions were normalized. The iris feature data was extracted by employing 2D Gabor wavelet. Moreover, the iris feature data was encrypted and decrypted using the AES algorithm, and hence, effectively enhancing the security of the generative information of iris feature data. Experimental results show that the proposed method can extract iris feature information within ten seconds, and the data security coefficient is high thus the proposed method efficiently realizes the information hiding.

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A comprehensive review on iris image-based biometric system

Cited by 35 publications

References 143 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

Iris Location Algorithm Based on Union‐Find‐Set and Block Search

Generative Information Hiding of Iris Feature Data Based on Gaussian Fuzzy Algorithm

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