The Evolution of Biometric Authentication: A Deep Dive Into Multi-Modal Facial Recognition: A Review Case Study
This survey provides an insightful overview of recent advancements in facial recognition technology, mainly focusing on multi-modal face recognition and its applications in security biometrics and identity verification. Central to this study is the Sejong Face Database, among other prominent dataset...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10735153/ |
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| author | Mohamed Abdul-Al George Kumi Kyeremeh Rami Qahwaji Nazar T. Ali Raed A. Abd-Alhameed |
| author_facet | Mohamed Abdul-Al George Kumi Kyeremeh Rami Qahwaji Nazar T. Ali Raed A. Abd-Alhameed |
| author_sort | Mohamed Abdul-Al |
| collection | DOAJ |
| description | This survey provides an insightful overview of recent advancements in facial recognition technology, mainly focusing on multi-modal face recognition and its applications in security biometrics and identity verification. Central to this study is the Sejong Face Database, among other prominent datasets, which facilitates the exploration of intricate aspects of facial recognition, including hidden and heterogeneous face recognition, cross-modality analysis, and thermal-visible face recognition. This paper delves into the challenges of accurately identifying faces under various conditions and disguises, emphasising its significance in security systems and sensitive sectors like banking. The survey highlights novel contributions such as using Generative Adversarial Networks (GANs) to generate synthetic disguised faces, Convolutional Neural Networks (CNNs) for feature extractions, and Fuzzy Extractors to integrate biometric verification with cryptographic security. The paper also discusses the impact of quantum computing on encryption techniques and the potential of post-quantum cryptographic methods to secure biometric systems. This survey is a critical resource for understanding current research and prospects in biometric authentication, balancing technological advancements with ethical and privacy concerns in an increasingly digital society. |
| format | Article |
| id | doaj-art-c01a11f46460405ea2a9363546c45fad |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-c01a11f46460405ea2a9363546c45fad2025-08-20T01:54:55ZengIEEEIEEE Access2169-35362024-01-011217901017903810.1109/ACCESS.2024.348655210735153The Evolution of Biometric Authentication: A Deep Dive Into Multi-Modal Facial Recognition: A Review Case StudyMohamed Abdul-Al0https://orcid.org/0000-0003-3606-3567George Kumi Kyeremeh1https://orcid.org/0009-0009-6995-8624Rami Qahwaji2https://orcid.org/0000-0002-8637-1130Nazar T. Ali3https://orcid.org/0000-0003-2991-9451Raed A. Abd-Alhameed4https://orcid.org/0000-0003-2972-9965Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, U.K.Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, U.K.Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, U.K.Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, United Arab EmiratesFaculty of Engineering and Digital Technologies, University of Bradford, Bradford, U.K.This survey provides an insightful overview of recent advancements in facial recognition technology, mainly focusing on multi-modal face recognition and its applications in security biometrics and identity verification. Central to this study is the Sejong Face Database, among other prominent datasets, which facilitates the exploration of intricate aspects of facial recognition, including hidden and heterogeneous face recognition, cross-modality analysis, and thermal-visible face recognition. This paper delves into the challenges of accurately identifying faces under various conditions and disguises, emphasising its significance in security systems and sensitive sectors like banking. The survey highlights novel contributions such as using Generative Adversarial Networks (GANs) to generate synthetic disguised faces, Convolutional Neural Networks (CNNs) for feature extractions, and Fuzzy Extractors to integrate biometric verification with cryptographic security. The paper also discusses the impact of quantum computing on encryption techniques and the potential of post-quantum cryptographic methods to secure biometric systems. This survey is a critical resource for understanding current research and prospects in biometric authentication, balancing technological advancements with ethical and privacy concerns in an increasingly digital society.https://ieeexplore.ieee.org/document/10735153/Facial recognition (FR)multi-modal face recognitionsecurity biometricsidentity verificationSejong face databasedeep learning techniques |
| spellingShingle | Mohamed Abdul-Al George Kumi Kyeremeh Rami Qahwaji Nazar T. Ali Raed A. Abd-Alhameed The Evolution of Biometric Authentication: A Deep Dive Into Multi-Modal Facial Recognition: A Review Case Study IEEE Access Facial recognition (FR) multi-modal face recognition security biometrics identity verification Sejong face database deep learning techniques |
| title | The Evolution of Biometric Authentication: A Deep Dive Into Multi-Modal Facial Recognition: A Review Case Study |
| title_full | The Evolution of Biometric Authentication: A Deep Dive Into Multi-Modal Facial Recognition: A Review Case Study |
| title_fullStr | The Evolution of Biometric Authentication: A Deep Dive Into Multi-Modal Facial Recognition: A Review Case Study |
| title_full_unstemmed | The Evolution of Biometric Authentication: A Deep Dive Into Multi-Modal Facial Recognition: A Review Case Study |
| title_short | The Evolution of Biometric Authentication: A Deep Dive Into Multi-Modal Facial Recognition: A Review Case Study |
| title_sort | evolution of biometric authentication a deep dive into multi modal facial recognition a review case study |
| topic | Facial recognition (FR) multi-modal face recognition security biometrics identity verification Sejong face database deep learning techniques |
| url | https://ieeexplore.ieee.org/document/10735153/ |
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