Performance of Multimodal Biometric Systems Using Face and Fingerprints (Short Survey)

Mohamed Abdul-Al; George Kumi Kyeremeh; Naser Ojaroudi Parchin; Raed A. Abd-Alhameed; Rami Qahwaji; Jonathan Rodriguez

doi:10.1109/CAMAD52502.2021.9617766

Performance of Multimodal Biometric Systems Using Face and Fingerprints (Short Survey)

Mohamed Abdul-Al, George Kumi Kyeremeh, Naser Ojaroudi Parchin, Raed A. Abd-Alhameed, Rami Qahwaji, Jonathan Rodriguez

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

Abstract

Biometric authentication is the science and engineering of assessing and evaluating bioinformatics from the human body in order to increase system security by providing reliable and accurate behaviors and classifiers for personal identification and authentication. Its solutions are widely used in industries, governments, and the military. This paper reviews the multimodal biometric systems that integrated both faces and fingerprints as well as shows which one has the best accuracy and hardware complexity with the methods and databases. Several methods have been used in multimodal biometric systems such as KNN (K-Nearest Neighbor), CNN (Convolutional Neural Network), PCA (Principal Component Analysis), and so on. A multimodal biometric system for face and fingerprints that uses an FoM (Figure of Merit) to compare and show between the articles the best accuracy that have used multimodal biometric system face and fingerprints methods. The best performance has been found is 99.43% by using the cascade multimodal method.

Original language	English
Title of host publication	2021 IEEE 26th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2021 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	978-1-6654-1779-2, 978-1-6654-1780-8
DOIs	https://doi.org/10.1109/CAMAD52502.2021.9617766
Publication status	Published - 6 Dec 2021
Externally published	Yes
Event	26th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2021 - Porto, Portugal Duration: 25 Oct 2021 → 27 Oct 2021

Conference

Conference	26th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2021
Country/Territory	Portugal
City	Porto
Period	25/10/21 → 27/10/21

Keywords

accuracy
Biometric
Cascade multimodal
CNN
Face
Fingerprint
FoM
KNN
LDA
loss
Multimodal
PCA

Access to Document

10.1109/CAMAD52502.2021.9617766

Cite this

Abdul-Al, M., Kyeremeh, G. K., Parchin, N. O., Abd-Alhameed, R. A., Qahwaji, R., & Rodriguez, J. (2021). Performance of Multimodal Biometric Systems Using Face and Fingerprints (Short Survey). In 2021 IEEE 26th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2021 - Proceedings Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAMAD52502.2021.9617766

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title = "Performance of Multimodal Biometric Systems Using Face and Fingerprints (Short Survey)",

abstract = "Biometric authentication is the science and engineering of assessing and evaluating bioinformatics from the human body in order to increase system security by providing reliable and accurate behaviors and classifiers for personal identification and authentication. Its solutions are widely used in industries, governments, and the military. This paper reviews the multimodal biometric systems that integrated both faces and fingerprints as well as shows which one has the best accuracy and hardware complexity with the methods and databases. Several methods have been used in multimodal biometric systems such as KNN (K-Nearest Neighbor), CNN (Convolutional Neural Network), PCA (Principal Component Analysis), and so on. A multimodal biometric system for face and fingerprints that uses an FoM (Figure of Merit) to compare and show between the articles the best accuracy that have used multimodal biometric system face and fingerprints methods. The best performance has been found is 99.43% by using the cascade multimodal method. ",

keywords = "accuracy, Biometric, Cascade multimodal, CNN, Face, Fingerprint, FoM, KNN, LDA, loss, Multimodal, PCA",

author = "Mohamed Abdul-Al and Kyeremeh, {George Kumi} and Parchin, {Naser Ojaroudi} and Abd-Alhameed, {Raed A.} and Rami Qahwaji and Jonathan Rodriguez",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 26th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2021 ; Conference date: 25-10-2021 Through 27-10-2021",

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Abdul-Al, M, Kyeremeh, GK, Parchin, NO, Abd-Alhameed, RA, Qahwaji, R & Rodriguez, J 2021, Performance of Multimodal Biometric Systems Using Face and Fingerprints (Short Survey). in 2021 IEEE 26th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 26th IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2021, Porto, Portugal, 25/10/21. https://doi.org/10.1109/CAMAD52502.2021.9617766

Performance of Multimodal Biometric Systems Using Face and Fingerprints (Short Survey). / Abdul-Al, Mohamed; Kyeremeh, George Kumi; Parchin, Naser Ojaroudi et al.
2021 IEEE 26th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2021.

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

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AB - Biometric authentication is the science and engineering of assessing and evaluating bioinformatics from the human body in order to increase system security by providing reliable and accurate behaviors and classifiers for personal identification and authentication. Its solutions are widely used in industries, governments, and the military. This paper reviews the multimodal biometric systems that integrated both faces and fingerprints as well as shows which one has the best accuracy and hardware complexity with the methods and databases. Several methods have been used in multimodal biometric systems such as KNN (K-Nearest Neighbor), CNN (Convolutional Neural Network), PCA (Principal Component Analysis), and so on. A multimodal biometric system for face and fingerprints that uses an FoM (Figure of Merit) to compare and show between the articles the best accuracy that have used multimodal biometric system face and fingerprints methods. The best performance has been found is 99.43% by using the cascade multimodal method.

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Abdul-Al M, Kyeremeh GK, Parchin NO, Abd-Alhameed RA, Qahwaji R, Rodriguez J. Performance of Multimodal Biometric Systems Using Face and Fingerprints (Short Survey). In 2021 IEEE 26th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2021 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2021 doi: 10.1109/CAMAD52502.2021.9617766