Generative AI-Based Real-Time Face Aging Simulation for Biometric Systems

Facial recognition is, therefore, a crucial aspect of biometric systems used when authenticating as well as verifying people’s identity. But here natural aging increases a number of difficulties concerning accuracy and long-term reliability of the control systems stated above. In this paper, a new m...

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Bibliographic Details
Published inE3S web of conferences Vol. 619; p. 3004
Main Authors R J, Anandhi, Jain, Alok, Ravali Reddy, B., Divetia, Kanan, Salman, Zahraa N., Devi Tellapati, Anuradha
Format Journal Article Conference Proceeding
LanguageEnglish
Published Les Ulis EDP Sciences 01.01.2025
Subjects
Accuracy
age progression
age regression
Aging
Aging (natural)
aging simulation
biometric systems
Biometrics
Control systems
deep learning
Face recognition
gan
generative ai
Generative artificial intelligence
image synthesis
live facial recognition
Pattern recognition
Real time
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Summary:Facial recognition is, therefore, a crucial aspect of biometric systems used when authenticating as well as verifying people’s identity. But here natural aging increases a number of difficulties concerning accuracy and long-term reliability of the control systems stated above. In this paper, a new method of real-time face aging simulation in the context of aging variance of biometric systems using Generative AI; specifically, GANs, is proposed. The proposed model tries to use generative AI in generation of improved synthetics with modified age appearance, allowing biometric systems to capture aging or antiaging changes in facial features. This approach is assessed experimentally from one facial database to another datasets and the principal area of interest is the future recognition accuracy of faces in the long run with respect to age groups. This work also looks at the strength and robustness of the model for real-time problems. The outcomes presented here show that applying generative AI-based system as a paradigm improves the performance of the biometric system specifically for addressing aging variations thus proposing a valuable solution to age- related biometric problems. The paper also considers some possible consequences for security, privacy, and concerns to practical application in real systems.
Bibliography:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISSN:2267-1242
2555-0403
2267-1242
DOI:10.1051/e3sconf/202561903004