Timing for securing the biometric template transformation based on supervised learning using Double Random Phase Encoding Method

Background: Among optical encryption techniques, Double Random Phase Encoding (DRPE) is one of the most widely used. Individual identities and the process of recognition remain essential to ensuring proper data access security.Aim: The study aims to optimize an approach that ensures the significant performance effectiveness of the cancelable biometric methods for different templates and the associated time taken to transform biometric data.Problem: This study is majorly concerned about the performance effectiveness of cancelable biometric methods that measure the likelihood that an authorized effort may be mistakenly rejected as unauthorized. Also, when compromised, several non-renewability safety challenges arise, and insufficient matching performance templates are required to build a security protection method.Method and material. The study uses supervised learning for the Double Random Phase Encoding Method (DRPE), a 4F optical encryption system, and 20 randomly chosen photos from the ORL database of faces.Results. The result based on the supervised learning for the Double Random Phase Encoding Method revealed false positive rates for both the fingerprint and face templates.Conclusion. The study concluded that the performance effectiveness of the cancelable biometric in this study has a false positive rate likelihood that an authorized effort may not be mistakenly rejected as an unauthorized one.

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Publication Details

Journal
EAI endorsed transactions on intelligent systems and machine learning applications.
Published
2025-06-18
DOI
https://doi.org/10.4108/eetismla.8773
Primary Topic
Biometric Identification and Security
Type
article
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0.00
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article

Timing for securing the biometric template transformation based on supervised learning using Double Random Phase Encoding Method

Pascal Muam Mah, Mahmoud Nasr
EAI endorsed transactions on intelligent systems and machine learning applications.
Biometric Identification and Security
article

Timing for securing the biometric template transformation based on supervised learning using Double Random Phase Encoding Method

Pascal Muam Mah, Mahmoud Nasr
article en

Abstract

Background: Among optical encryption techniques, Double Random Phase Encoding (DRPE) is one of the most widely used. Individual identities and the process of recognition remain essential to ensuring proper data access security.Aim: The study aims to optimize an approach that ensures the significant performance effectiveness of the cancelable biometric methods for different templates and the associated time taken to transform biometric data.Problem: This study is majorly concerned about the performance effectiveness of cancelable biometric methods that measure the likelihood that an authorized effort may be mistakenly rejected as unauthorized. Also, when compromised, several non-renewability safety challenges arise, and insufficient matching performance templates are required to build a security protection method.Method and material. The study uses supervised learning for the Double Random Phase Encoding Method (DRPE), a 4F optical encryption system, and 20 randomly chosen photos from the ORL database of faces.Results. The result based on the supervised learning for the Double Random Phase Encoding Method revealed false positive rates for both the fingerprint and face templates.Conclusion. The study concluded that the performance effectiveness of the cancelable biometric in this study has a false positive rate likelihood that an authorized effort may not be mistakenly rejected as an unauthorized one.

EAI endorsed transactions on intelligent systems and machine learning applications.Vol. 2
Jagiellonian University (PL), AGH University of Krakow (PL)
Openalex Percentile: Top 84%
Biometric Identification and Security
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Timing for securing the biometric template transformation based on supervised learning using Double Random Phase Encoding Method — Pascal Muam Mah, Mahmoud Nasr · EAI endorsed transactions on intelligent systems and machine learning applications. (2025) | TGRS Research Map | TGRS