The Effects of Gender Factor and Diabetes Mellitus on the Iris Recognition System's Accuracy and Reliability

Mohammadreza Azimi; Seyed Ahmad Rasoulinejad; Andrzej Pacut

doi:10.23919/SPA.2019.8936757

The Effects of Gender Factor and Diabetes Mellitus on the Iris Recognition System's Accuracy and Reliability

Mohammadreza Azimi (Corresponding Author), Seyed Ahmad Rasoulinejad (Corresponding Author), Andrzej Pacut

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

We present a performance analysis of iris recognition system for healthy and diabetes affected irises, separately for feMale and Male users. The database consists of 546 pictures from 162 healthy irises (62% feMale users, 38% Male users) and 772 iris images from 181 diabetic eyes with a clearly visible iris pattern (80% feMale users, 20% Male users), Adaptive weighted Hough ellipsopolar transform technique was used for iris segmentation, and then three popular iris encoding algorithms were implemented. Bhattacharyya distance was used for comparison of diabetic and healthy irises of men and women. We founf that for Male users, diabetic effects on the performance of the system are more intense that for feMale users.

Original language	English
Title of host publication	23rd Signal Processing
Subtitle of host publication	Algorithms, Architectures, Arrangements, and Applications, SPA 2019
Publisher	IEEE Computer Society
Pages	273-278
Number of pages	6
ISBN (Electronic)	978-836206536-3
DOIs	https://doi.org/10.23919/SPA.2019.8936757
Publication status	Published - Sept 2019
Externally published	Yes
Event	23rd Signal Processing: Algorithms, Architectures, Arrangements, and Applications - Poznan University of Technology, Poznan, Poland Duration: 18 Sept 2019 → 20 Sept 2019 Conference number: 23 http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=87960&copyownerid=87608

Publication series

Name	Signal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA
Volume	2019-September
ISSN (Print)	2326-0262
ISSN (Electronic)	2326-0319

Conference

Conference	23rd Signal Processing: Algorithms, Architectures, Arrangements, and Applications
Abbreviated title	SPA 2019
Country/Territory	Poland
City	Poznan
Period	18/09/19 → 20/09/19
Internet address	http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=87960&copyownerid=87608

Keywords*

Biometrics
diabetes influence on iris recognition
gender-dependency
Iris Recognition
iris recognition reliability

Field of Science*

1.6 Biological sciences
3.2 Clinical medicine
2.2 Electrical engineering, Electronic engineering, Information engineering

Publication Type*

3.1. Articles or chapters in proceedings/scientific books indexed in Web of Science and/or Scopus database

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.23919/SPA.2019.8936757

https://ieeexplore.ieee.org/document/8936757

Cite this

Azimi, M., Rasoulinejad, S. A., & Pacut, A. (2019). The Effects of Gender Factor and Diabetes Mellitus on the Iris Recognition System's Accuracy and Reliability. In 23rd Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2019 (pp. 273-278). Article 8936757 (Signal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA; Vol. 2019-September). IEEE Computer Society. https://doi.org/10.23919/SPA.2019.8936757

Azimi, Mohammadreza ; Rasoulinejad, Seyed Ahmad ; Pacut, Andrzej. / The Effects of Gender Factor and Diabetes Mellitus on the Iris Recognition System's Accuracy and Reliability. 23rd Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2019. IEEE Computer Society, 2019. pp. 273-278 (Signal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA).

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title = "The Effects of Gender Factor and Diabetes Mellitus on the Iris Recognition System's Accuracy and Reliability",

abstract = "We present a performance analysis of iris recognition system for healthy and diabetes affected irises, separately for feMale and Male users. The database consists of 546 pictures from 162 healthy irises (62% feMale users, 38% Male users) and 772 iris images from 181 diabetic eyes with a clearly visible iris pattern (80% feMale users, 20% Male users), Adaptive weighted Hough ellipsopolar transform technique was used for iris segmentation, and then three popular iris encoding algorithms were implemented. Bhattacharyya distance was used for comparison of diabetic and healthy irises of men and women. We founf that for Male users, diabetic effects on the performance of the system are more intense that for feMale users.",

keywords = "Biometrics, diabetes influence on iris recognition, gender-dependency, Iris Recognition, iris recognition reliability",

author = "Mohammadreza Azimi and Rasoulinejad, {Seyed Ahmad} and Andrzej Pacut",

note = "Publisher Copyright: {\textcopyright} 2019 Division of Signal Processing and Electronic Systems, Poznan University of Technology (DSPES PUT).; 23rd Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2019 ; Conference date: 18-09-2019 Through 20-09-2019",

year = "2019",

month = sep,

doi = "10.23919/SPA.2019.8936757",

language = "English",

series = "Signal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA",

publisher = "IEEE Computer Society",

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Azimi, M, Rasoulinejad, SA & Pacut, A 2019, The Effects of Gender Factor and Diabetes Mellitus on the Iris Recognition System's Accuracy and Reliability. in 23rd Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2019., 8936757, Signal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA, vol. 2019-September, IEEE Computer Society, pp. 273-278, 23rd Signal Processing: Algorithms, Architectures, Arrangements, and Applications, Poznan, Poland, 18/09/19. https://doi.org/10.23919/SPA.2019.8936757

The Effects of Gender Factor and Diabetes Mellitus on the Iris Recognition System's Accuracy and Reliability. / Azimi, Mohammadreza (Corresponding Author); Rasoulinejad, Seyed Ahmad (Corresponding Author); Pacut, Andrzej.
23rd Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2019. IEEE Computer Society, 2019. p. 273-278 8936757 (Signal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA; Vol. 2019-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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N1 - Conference code: 23

PY - 2019/9

Y1 - 2019/9

N2 - We present a performance analysis of iris recognition system for healthy and diabetes affected irises, separately for feMale and Male users. The database consists of 546 pictures from 162 healthy irises (62% feMale users, 38% Male users) and 772 iris images from 181 diabetic eyes with a clearly visible iris pattern (80% feMale users, 20% Male users), Adaptive weighted Hough ellipsopolar transform technique was used for iris segmentation, and then three popular iris encoding algorithms were implemented. Bhattacharyya distance was used for comparison of diabetic and healthy irises of men and women. We founf that for Male users, diabetic effects on the performance of the system are more intense that for feMale users.

AB - We present a performance analysis of iris recognition system for healthy and diabetes affected irises, separately for feMale and Male users. The database consists of 546 pictures from 162 healthy irises (62% feMale users, 38% Male users) and 772 iris images from 181 diabetic eyes with a clearly visible iris pattern (80% feMale users, 20% Male users), Adaptive weighted Hough ellipsopolar transform technique was used for iris segmentation, and then three popular iris encoding algorithms were implemented. Bhattacharyya distance was used for comparison of diabetic and healthy irises of men and women. We founf that for Male users, diabetic effects on the performance of the system are more intense that for feMale users.

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Azimi M, Rasoulinejad SA, Pacut A. The Effects of Gender Factor and Diabetes Mellitus on the Iris Recognition System's Accuracy and Reliability. In 23rd Signal Processing: Algorithms, Architectures, Arrangements, and Applications, SPA 2019. IEEE Computer Society. 2019. p. 273-278. 8936757. (Signal Processing - Algorithms, Architectures, Arrangements, and Applications Conference Proceedings, SPA). doi: 10.23919/SPA.2019.8936757

The Effects of Gender Factor and Diabetes Mellitus on the Iris Recognition System's Accuracy and Reliability

Abstract

Publication series

Conference

Keywords*

Field of Science*

Publication Type*

UN SDGs

Access to Document

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