OSIRIS: An open source iris recognition software

Othman, Nadia; Dorizzi, Bernadette; Garcia-Salicetti, Sonia

doi:10.1016/j.patrec.2015.09.002

Cited by 161 publications

(126 citation statements)

References 22 publications

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“…The iris is detected from the input image using commercial software Osiris [23]. A segmentation mask occludes the eyelids, eyelashes and specular reflection portions of the iris image which are not useful for gender classification.…”

Section: Iris Segmentation and Normalisationmentioning

confidence: 99%

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Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features

Tapia

Arellano

2019

2019 International Conference on Biometrics (ICB)

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Soft biometric information such as gender can contribute to many applications like as identification and security. This paper explores the use of a Binary Statistical Features (BSIF) algorithm for classifying gender from iris texture images captured with NIR sensors. It uses the same pipeline for iris recognition systems consisting of iris segmentation, normalisation and then classification. Experiments show that applying BSIF is not straightforward since it can create artificial textures causing misclassification. In order to overcome this limitation, a new set of filters was trained from eye images and different sized filters with padding bands were tested on a subject-disjoint database. A Modified-BSIF (MBSIF) method was implemented. The latter achieved better gender classification results (94.6% and 91.33% for the left and right eye respectively). These results are competitive with the state of the art in gender classification. In an additional contribution, a novel gender labelled database was created and it will be available upon request.

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Section: Iris Segmentation and Normalisationmentioning

confidence: 99%

“…The size of the normalised iris can significantly influence the iris recognition rate. In this work, a rectangular image of 20(r)x 240(θ) created using Osiris software [23] with automatic segmentation is used for all experiments.…”

Section: Iris Segmentation and Normalisationmentioning

confidence: 99%

Gender Classification from Iris Texture Images Using a New Set of Binary Statistical Image Features

Tapia

Arellano

2019

2019 International Conference on Biometrics (ICB)

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“…These security applications are used for access control, banking, border control, and forensics. Nowadays a special consideration is given to iris recognition system due to its high performance and reliability for identification [1]. The system relies on the iris pattern (texture) which if modified will affect the recognition performance [2].…”

Section: Introductionmentioning

confidence: 99%

A Review of Iris Recognition Algorithms

Ghali

Jamel

Mohamad

et al. 2017

JOIV : Int. J. Inform. Visualization

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Abstract-With the prominent needs for security and reliable mode of identification in biometric system. Iris recognition has become reliable method for personal identification nowadays. The system has been used for years in many commercial and government applications that allow access control in places such as office, laboratory, armoury, automated teller machines (ATMs), and border control in airport. The aim of the paper is to review iris recognition algorithms. Iris recognition system consists of four main stages which are segmentation, normalization, feature extraction and matching. Based on the findings, the Hough transform, rubber sheet model, wavelet, Gabor filter, and hamming distance are the most common used algorithms in iris recognition stages. This shows that, the algorithms have the potential and capability to enhanced iris recognition system.

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“…Therefore we did not make an exhaustive comparison of our system with the state of the art and we limited our experiments to illustrative wellchosen cases. Namely we selected OSIRIS-V4 reference system [14] , which finds the contours directly on the grey-level image thanks to a Viterbi approach and systems in the literature, which perform pixel classification into regions and find a circular contour on the resulting binarized images [16,[21][22][23] . Note that, in our case the presegmentation module is unsupervised contrary to what is done in these papers.…”

Section: Introductionmentioning

confidence: 99%