A review of iris recognition algorithms

Ng, Richard Yew Fatt; Tay, Yong Haur; Mok, Kai-Ming

doi:10.1109/itsim.2008.4631656

Cited by 29 publications

(14 citation statements)

References 25 publications

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“…The main phases of the iris recognition system are image acquisition, image pre-processing, feature extraction, and classification [22]. The most important algorithms in every iris recognition phase will be discussed in this section .…”

Section: Related Workmentioning

confidence: 99%

“…The preprocessing stage is required for the iris image to get a useful iris region. Image preprocessing is included two phases: iris segmentation and iris normalization [22].…”

Section: Image Preprocessingmentioning

confidence: 99%

“…It is essential to separate the iris part from the other parts of the eye image also to find outer and inner boundaries of the iris [23]. Iris segmentation is significant because accurate iris part is required to create the templates for correct classification [22]. Several algorithms have been used for iris segmentation and some of them are presented here:…”

Section: Iris Segmentationmentioning

confidence: 99%

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Transfer Learning with Convolutional Neural Networks for IRIS Recognition

Alaslni¹,

Elrefaei²

2019

IJAIA

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Iris is one of the common biometrics used for identity authentication. It has the potential to recognize persons with a high degree of assurance. Extracting effective features is the most important stage in the iris recognition system. Different features have been used to perform iris recognition system. A lot of them are based on hand-crafted features designed by biometrics experts. According to the achievement of deep learning in object recognition problems, the features learned by the Convolutional Neural Network (CNN) have gained great attention to be used in the iris recognition system. In this paper, we proposed an effective iris recognition system by using transfer learning with Convolutional Neural Networks. The proposed system is implemented by fine-tuning a pre-trained convolutional neural network (VGG-16) for features extracting and classification. The performance of the iris recognition system is tested on four public databases IITD, iris databases CASIA-Iris-V1, CASIA-Iris-thousand and, CASIA-Iris-Interval. The results show that the proposed system is achieved a very high accuracy rate.

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Section: Related Workmentioning

confidence: 99%

“…The preprocessing stage is required for the iris image to get a useful iris region. Image preprocessing is included two phases: iris segmentation and iris normalization [22].…”

Section: Image Preprocessingmentioning

confidence: 99%

Section: Iris Segmentationmentioning

confidence: 99%

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Transfer Learning with Convolutional Neural Networks for IRIS Recognition

Alaslni¹,

Elrefaei²

2019

IJAIA

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“…In addition, relying on texture only and ignoring geometric and crack features allow implementing a simple and fast identification system. In addition, texture analysis has been proven to helpful for the extraction of features [3][4][5] [8]. Moreover, a system where textural properties of a tongue are used can be implemented in clinical medicine for diagnosis purposes [13].…”

Section: Related Workmentioning

confidence: 99%

“…Other traits and corresponding technologies are described in [2]. Recently, due to the importance of biometrics many comprehensive surveys and works have been published on fingerprint [3], palmprint [4], face [5] [6], retina [7], iris [8], speech recognition [9], gait [10], and behavioral human-computer interaction (HCI) [11].…”

Section: Introductionmentioning

confidence: 99%

Recognition of Tongueprint Textures for Personal Authentication: A Wavelet Approach

Lahmiri¹

2012

JAIT

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In order to verify tongueprint images, three approaches for texture analyses were considered and their performances are compared. They are wavelet transform, Gabor filter, and spectral analysis. In all approaches, six statistical measures are applied to the processed images to extract features. They are the mean, standard deviation, smoothness, third moment, uniformity, and entropy. Finally, k-nearest neighbour algorithm (k-NN) is used to classify tongue textures for verification purposes. The obtained recognition rates show that features extracted from wavelet analysis allow achieving the highest accuracy (92%) among the other approaches. On the other hand, features extracted from spectral images lead to the lowest recognition rate (75%). Features extracted from Gabor filter banks obtained 83%. Therefore, we conclude that wavelet-based features outperform Gabor and spectral-based features employed in the literature.

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