Investigation of an Innovative Approach for Identifying Human Face-Profile Using Explainable Artificial Intelligence

Sarathy, N; Alsawwaf, M; Chaczko, Z

Investigation of an Innovative Approach for Identifying Human Face-Profile Using Explainable Artificial Intelligence

Sarathy, N Alsawwaf, M

Chaczko, Z

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE 18th International Symposium on Intelligent Systems and Informatics (SISY), 2020, 00, pp. 155-160
Issue Date:: 2020-09-01

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Field	Value	Language
dc.contributor.author	Sarathy, N
dc.contributor.author	Alsawwaf, M https://orcid.org/0000-0003-0359-9808
dc.contributor.author	Chaczko, Z https://orcid.org/0000-0002-2816-7510
dc.date	2020-09-17
dc.date.accessioned	2021-06-09T05:53:44Z
dc.date.available	2021-06-09T05:53:44Z
dc.date.issued	2020-09-01
dc.identifier.citation	2020 IEEE 18th International Symposium on Intelligent Systems and Informatics (SISY), 2020, 00, pp. 155-160
dc.identifier.isbn	9781728173528
dc.identifier.issn	1949-047X
dc.identifier.issn	1949-0488
dc.identifier.uri	http://hdl.handle.net/10453/149469
dc.description.abstract	Human identification is a well-researched topic that keeps evolving. Advancement in technology has made it easy to train models or use ones that have been already created to detect several features of the human face. When it comes to identifying a human face from the side, there are many opportunities to advance the biometric identification research further. This paper investigates the human face identification based on their side profile by extracting the facial features and diagnosing the feature sets with geometric ratio expressions. These geometric ratio expressions are computed into feature vectors. The last stage involves the use of weighted means to measure similarity. This research addresses the problem of using an eXplainable Artificial Intelligence (XAI) approach. Findings from this research, based on a small data-set, conclude that the used approach offers encouraging results. Further investigation could have a significant impact on how face profiles can be identified. Performance of the proposed system is validated using metrics such as Precision, False Acceptance Rate, False Rejection Rate and True Positive Rate. Multiple simulations indicate an Equal Error Rate of 0.89.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE 18th International Symposium on Intelligent Systems and Informatics (SISY)
dc.relation.ispartof	IEEE 18th International Symposium on Intelligent Systems and Informatics
dc.relation.isbasedon	10.1109/SISY50555.2020.9217095
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Investigation of an Innovative Approach for Identifying Human Face-Profile Using Explainable Artificial Intelligence
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Serbia
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
pubs.consider-herdc	false
utslib.copyright.embargo	2022-10-08T00:00:00+1000Z
dc.date.updated	2021-06-09T05:53:43Z
pubs.finish-date	2020-09-19
pubs.publication-status	Published
pubs.start-date	2020-09-17
pubs.volume	00

Abstract:

Human identification is a well-researched topic that keeps evolving. Advancement in technology has made it easy to train models or use ones that have been already created to detect several features of the human face. When it comes to identifying a human face from the side, there are many opportunities to advance the biometric identification research further. This paper investigates the human face identification based on their side profile by extracting the facial features and diagnosing the feature sets with geometric ratio expressions. These geometric ratio expressions are computed into feature vectors. The last stage involves the use of weighted means to measure similarity. This research addresses the problem of using an eXplainable Artificial Intelligence (XAI) approach. Findings from this research, based on a small data-set, conclude that the used approach offers encouraging results. Further investigation could have a significant impact on how face profiles can be identified. Performance of the proposed system is validated using metrics such as Precision, False Acceptance Rate, False Rejection Rate and True Positive Rate. Multiple simulations indicate an Equal Error Rate of 0.89.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/149469