Performance evaluation of Independent Component Analysis in an iris recognition system

Imen Bouraoui; Salim Chitroub; Ahmed Bouridane

doi:10.1109/AICCSA.2010.5586977

Performance evaluation of Independent Component Analysis in an iris recognition system

Imen Bouraoui, Salim Chitroub, Ahmed Bouridane

Computer and Information Sciences

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

Abstract

The overall performance of any iris recognition system relies on the performance of its components, which are preprocessing, feature extraction and matching. Feature extraction is the important step of such recognition system, but it is strongly dependent on the pre-processing step that is consisting of localising and normalising the iris. In this paper, Independent Component Analysis (ICA), which is a recently developed statistical method for data analysis, is applied for extracting the features for iris region of interest that are statistically independent. Based on some mathematical criteria, the performance of ICA is evaluated by using two different subsets of CASIA-V3 iris image database. The obtained results are convincing and some future improved research works are subsequently envisaged.

Original language	English
DOIs	https://doi.org/10.1109/AICCSA.2010.5586977
Publication status	Published - May 2010
Event	2010 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) - Hammamet, Tunisia Duration: 1 May 2010 → …

Conference

Conference	2010 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)
Period	1/05/10 → …

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10.1109/AICCSA.2010.5586977

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title = "Performance evaluation of Independent Component Analysis in an iris recognition system",

abstract = "The overall performance of any iris recognition system relies on the performance of its components, which are preprocessing, feature extraction and matching. Feature extraction is the important step of such recognition system, but it is strongly dependent on the pre-processing step that is consisting of localising and normalising the iris. In this paper, Independent Component Analysis (ICA), which is a recently developed statistical method for data analysis, is applied for extracting the features for iris region of interest that are statistically independent. Based on some mathematical criteria, the performance of ICA is evaluated by using two different subsets of CASIA-V3 iris image database. The obtained results are convincing and some future improved research works are subsequently envisaged.",

keywords = "entropy, feature extraction, iris, iris recognition, random variables, transforms, vectors",

author = "Imen Bouraoui and Salim Chitroub and Ahmed Bouridane",

year = "2010",

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note = "2010 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) ; Conference date: 01-05-2010",

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T1 - Performance evaluation of Independent Component Analysis in an iris recognition system

AU - Bouraoui, Imen

AU - Chitroub, Salim

AU - Bouridane, Ahmed

PY - 2010/5

Y1 - 2010/5

N2 - The overall performance of any iris recognition system relies on the performance of its components, which are preprocessing, feature extraction and matching. Feature extraction is the important step of such recognition system, but it is strongly dependent on the pre-processing step that is consisting of localising and normalising the iris. In this paper, Independent Component Analysis (ICA), which is a recently developed statistical method for data analysis, is applied for extracting the features for iris region of interest that are statistically independent. Based on some mathematical criteria, the performance of ICA is evaluated by using two different subsets of CASIA-V3 iris image database. The obtained results are convincing and some future improved research works are subsequently envisaged.

AB - The overall performance of any iris recognition system relies on the performance of its components, which are preprocessing, feature extraction and matching. Feature extraction is the important step of such recognition system, but it is strongly dependent on the pre-processing step that is consisting of localising and normalising the iris. In this paper, Independent Component Analysis (ICA), which is a recently developed statistical method for data analysis, is applied for extracting the features for iris region of interest that are statistically independent. Based on some mathematical criteria, the performance of ICA is evaluated by using two different subsets of CASIA-V3 iris image database. The obtained results are convincing and some future improved research works are subsequently envisaged.

KW - entropy

KW - feature extraction

KW - iris

KW - iris recognition

KW - random variables

KW - transforms

KW - vectors

U2 - 10.1109/AICCSA.2010.5586977

DO - 10.1109/AICCSA.2010.5586977

M3 - Paper

T2 - 2010 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA)

Y2 - 1 May 2010

ER -

Performance evaluation of Independent Component Analysis in an iris recognition system

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