Ethnicity distinctiveness through iris texture features using Gabor filters

Gugulethu Mabuza-Hocquet; Fulufhelo Nelwamondo; Tshilidzi Marwala

doi:10.1007/978-3-319-54430-4_53

Ethnicity distinctiveness through iris texture features using Gabor filters

Gugulethu Mabuza-Hocquet, Fulufhelo Nelwamondo, Tshilidzi Marwala

Faculty of Engineering and the Built Environment

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

1 Citation (Scopus)

Abstract

Research in iris biometrics has been focused on utilizing iris features as a means of identity verification and authentication. However, not enough research work has been done to explore iris textures to determine soft biometrics such as gender and ethnicity. Researchers have reported that iris texture features contain information that is inclined to human genetics and is highly discriminative between different eyes of different ethnicities. This work applies image processing and machine learning techniques by designing a bank of Gabor filters to develop a model that extracts iris textures to distinctively differentiate individuals according to ethnicity. From a database of 30 subjects with 120 images, results show that the mean amplitude computed from Gabor magnitude and phase provides a correct ethnic distinction of 93.33% between African Black and Caucasian subjects. The compactness of the produced feature vector promises a suitable integration with an existing iris recognition system.

Original language	English
Title of host publication	Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings
Editors	Satoshi Tojo, Le Minh Nguyen, Ngoc Thanh Nguyen, Bogdan Trawinski
Publisher	Springer Verlag
Pages	551-560
Number of pages	10
ISBN (Print)	9783319544298
DOIs	https://doi.org/10.1007/978-3-319-54430-4_53
Publication status	Published - 2017
Event	9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017 - Kanazawa, Japan Duration: 3 Apr 2017 → 5 Apr 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10192 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017
Country/Territory	Japan
City	Kanazawa
Period	3/04/17 → 5/04/17

Keywords

Ethnic distinction
Gabor filters
Iris segmentation
Iris texture extraction
Soft biometrics

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-54430-4_53

Cite this

Mabuza-Hocquet, G., Nelwamondo, F., & Marwala, T. (2017). Ethnicity distinctiveness through iris texture features using Gabor filters. In S. Tojo, L. M. Nguyen, N. T. Nguyen, & B. Trawinski (Eds.), Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings (pp. 551-560). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10192 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-54430-4_53

Mabuza-Hocquet, Gugulethu ; Nelwamondo, Fulufhelo ; Marwala, Tshilidzi. / Ethnicity distinctiveness through iris texture features using Gabor filters. Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. editor / Satoshi Tojo ; Le Minh Nguyen ; Ngoc Thanh Nguyen ; Bogdan Trawinski. Springer Verlag, 2017. pp. 551-560 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{71c17224220c4afea3e6ef3915698ba8,

title = "Ethnicity distinctiveness through iris texture features using Gabor filters",

abstract = "Research in iris biometrics has been focused on utilizing iris features as a means of identity verification and authentication. However, not enough research work has been done to explore iris textures to determine soft biometrics such as gender and ethnicity. Researchers have reported that iris texture features contain information that is inclined to human genetics and is highly discriminative between different eyes of different ethnicities. This work applies image processing and machine learning techniques by designing a bank of Gabor filters to develop a model that extracts iris textures to distinctively differentiate individuals according to ethnicity. From a database of 30 subjects with 120 images, results show that the mean amplitude computed from Gabor magnitude and phase provides a correct ethnic distinction of 93.33% between African Black and Caucasian subjects. The compactness of the produced feature vector promises a suitable integration with an existing iris recognition system.",

keywords = "Ethnic distinction, Gabor filters, Iris segmentation, Iris texture extraction, Soft biometrics",

author = "Gugulethu Mabuza-Hocquet and Fulufhelo Nelwamondo and Tshilidzi Marwala",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2017.; 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017 ; Conference date: 03-04-2017 Through 05-04-2017",

year = "2017",

doi = "10.1007/978-3-319-54430-4_53",

language = "English",

isbn = "9783319544298",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "551--560",

editor = "Satoshi Tojo and Nguyen, {Le Minh} and Nguyen, {Ngoc Thanh} and Bogdan Trawinski",

booktitle = "Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings",

address = "Germany",

}

Mabuza-Hocquet, G, Nelwamondo, F & Marwala, T 2017, Ethnicity distinctiveness through iris texture features using Gabor filters. in S Tojo, LM Nguyen, NT Nguyen & B Trawinski (eds), Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10192 LNAI, Springer Verlag, pp. 551-560, 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017, Kanazawa, Japan, 3/04/17. https://doi.org/10.1007/978-3-319-54430-4_53

Ethnicity distinctiveness through iris texture features using Gabor filters. / Mabuza-Hocquet, Gugulethu; Nelwamondo, Fulufhelo; Marwala, Tshilidzi.
Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. ed. / Satoshi Tojo; Le Minh Nguyen; Ngoc Thanh Nguyen; Bogdan Trawinski. Springer Verlag, 2017. p. 551-560 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10192 LNAI).

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

TY - GEN

T1 - Ethnicity distinctiveness through iris texture features using Gabor filters

AU - Mabuza-Hocquet, Gugulethu

AU - Nelwamondo, Fulufhelo

AU - Marwala, Tshilidzi

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017

Y1 - 2017

N2 - Research in iris biometrics has been focused on utilizing iris features as a means of identity verification and authentication. However, not enough research work has been done to explore iris textures to determine soft biometrics such as gender and ethnicity. Researchers have reported that iris texture features contain information that is inclined to human genetics and is highly discriminative between different eyes of different ethnicities. This work applies image processing and machine learning techniques by designing a bank of Gabor filters to develop a model that extracts iris textures to distinctively differentiate individuals according to ethnicity. From a database of 30 subjects with 120 images, results show that the mean amplitude computed from Gabor magnitude and phase provides a correct ethnic distinction of 93.33% between African Black and Caucasian subjects. The compactness of the produced feature vector promises a suitable integration with an existing iris recognition system.

AB - Research in iris biometrics has been focused on utilizing iris features as a means of identity verification and authentication. However, not enough research work has been done to explore iris textures to determine soft biometrics such as gender and ethnicity. Researchers have reported that iris texture features contain information that is inclined to human genetics and is highly discriminative between different eyes of different ethnicities. This work applies image processing and machine learning techniques by designing a bank of Gabor filters to develop a model that extracts iris textures to distinctively differentiate individuals according to ethnicity. From a database of 30 subjects with 120 images, results show that the mean amplitude computed from Gabor magnitude and phase provides a correct ethnic distinction of 93.33% between African Black and Caucasian subjects. The compactness of the produced feature vector promises a suitable integration with an existing iris recognition system.

KW - Ethnic distinction

KW - Gabor filters

KW - Iris segmentation

KW - Iris texture extraction

KW - Soft biometrics

UR - http://www.scopus.com/inward/record.url?scp=85018461364&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-54430-4_53

DO - 10.1007/978-3-319-54430-4_53

M3 - Conference contribution

AN - SCOPUS:85018461364

SN - 9783319544298

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 551

EP - 560

BT - Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings

A2 - Tojo, Satoshi

A2 - Nguyen, Le Minh

A2 - Nguyen, Ngoc Thanh

A2 - Trawinski, Bogdan

PB - Springer Verlag

T2 - 9th Asian Conference on Intelligent Information and Database Systems, ACIIDS 2017

Y2 - 3 April 2017 through 5 April 2017

ER -

Mabuza-Hocquet G, Nelwamondo F, Marwala T. Ethnicity distinctiveness through iris texture features using Gabor filters. In Tojo S, Nguyen LM, Nguyen NT, Trawinski B, editors, Intelligent Information and Database Systems - 9th Asian Conference, ACIIDS 2017, Proceedings. Springer Verlag. 2017. p. 551-560. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-54430-4_53

Ethnicity distinctiveness through iris texture features using Gabor filters

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this