An Iris Recognition Algorithm for Identity Authentication

Guo, Qiaoli; Zheng, Jun

doi:10.1109/icitbs.2018.00162

Cited by 10 publications

(3 citation statements)

References 6 publications

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“… Guo & Zheng (2018) suggested a gray projection algorithm for establishing identity authentication. They used improved Daugman and Point Hough Transform (PHT) algorithms to place the outer and inner boundaries, respectively.…”

Section: Related Literaturementioning

confidence: 99%

Iris recognition approach for identity verification with DWT and multiclass SVM

El-Sayed

Abdel-Latif

2022

PeerJ Computer Science

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The iris has been proven to be one of the most stable and accurate biometrics. It has been widely used in recognition systems to determine the identity of the individual who attempts to access secured or restricted areas (e.g., airports, ATM, datacenters). An iris recognition (IR) technique for identity authentication/verification is proposed in this research. Iris image pre-processing, which includes iris segmentation, normalization, and enhancement, is followed by feature extraction, and matching. First, the iris image is segmented using the Hough Transform technique. The Daugman’s rubber sheet model is the used to normalize the segmented iris area. Then, using enhancing techniques (such as histogram equalization), Gabor wavelets and Discrete Wavelets Transform should be used to precisely extract the prominent characteristics. A multiclass Support Vector Machine (SVM) is used to assess the similarity of the images. The suggested method is evaluated using the IITD iris dataset, which is one of the most often used iris datasets. The benefit of the suggested method is that it reduces the number of features in each image to only 88. Experiments revealed that the proposed method was capable of collecting a moderate quantity of useful features and outperformed other methods. Furthermore, the proposed method's recognition accuracy was found to be 98.92% on tested data.

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Section: Related Literaturementioning

confidence: 99%

Iris recognition approach for identity verification with DWT and multiclass SVM

El-Sayed

Abdel-Latif

2022

PeerJ Computer Science

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“…A fast and efficient algorithm, which is robust against interference was proposed in [15], which uses Point Hough Transform (PHT) on pretreated iris images. This algorithm is an improvement on Daugman algorithm to segment images.…”

Section: Matchingmentioning

confidence: 99%

Investigation of iris segmentation techniques using active contours for non-cooperative iris recognition

Akinfende

Imoize

Ajose

2020

IJEECS

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<span>Iris image segmentation process based on graphical user interface (GUI) to accurately localize the iris structure is presented in this paper. The major challenge confronting the precision of an iris recognition model is how to determine the accuracy of the iris segmentation and localization. There are varying parameters that introduce constraints during feature extraction and these greatly affect the matching performance during iris localization. To this end, the Integro-differential operator, which involves the detection of inner and outer regions of the iris, and the circular hough transform, which is capable of detecting the circular boundary from the edge mapping were investigated, and an active contour model was evolved. In the evolved model, an emerging curve mapped with the zeros of the data set function is experimentally exploited. To demonstrate the suitability of the model for precise iris recognition, its parameters were compared against other related models. Simulation results show that the model has higher flexibility of substitution of images, and the images could be analyzed more accurately with less false rejections (FR) and false acceptance (FA) in comparison with the integro-differential operator. This implies that images could be analyzed faster using the evolved model, and easily substituted especially in situations where the need to care for numerous eye patients occur.</span>

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“…After the answer card is input into the system, there are many image processing functions available in MATLAB. It involves the functions of image filtering, denoising, grayscale processing, binary processing, image segmentation, image correction and so on [6][7].…”

Section: System Designmentioning

confidence: 99%

Answer Sheet Recognition System Design

Gao

Wang

2020

JSRR

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Examination is an indispensable way for students to verify their learning achievements and select talents. With the continuous development of computer technology, especially the emergence of answer sheets, computers have become the mainstream instead of manual examination. The main research content of this design is to use MATLAB software to identify and obtain the information in the answer sheet, import the completed answer sheet into the system, and use MATLAB software to preprocess the image to obtain an image that can be identified later. Through the system to identify the information in the answer card and compare the correct answers, finally realize the identification of the candidate's student number, identification of subjects, reminders of missing information, and calculation of scores and other functions. This article designed the GUI interface, which provided convenience for human-computer interaction, making the system operation more convenient and the results clear.

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An Iris Recognition Algorithm for Identity Authentication

Cited by 10 publications

References 6 publications

Iris recognition approach for identity verification with DWT and multiclass SVM

Iris recognition approach for identity verification with DWT and multiclass SVM

Investigation of iris segmentation techniques using active contours for non-cooperative iris recognition

Answer Sheet Recognition System Design

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