Biometric Security Systems Enhanced by AI: Exploring Concerns with AI Advancements in Facial Recognition and Other Biometric Systems have Security Implications and Vulnerabilities

Umang H Patel; Krish Gera1

1

Publication Date: 2024/07/09

Abstract: A new age of accuracy and efficiency, especially in face recognition and other biometric technologies, has been brought about in recent years by the integration of artificial intelligence (AI) into biometric security systems. The discussion extends to the security implications of AI-enhanced biometric systems, including their susceptibility to threats such as spoofing and adversarial attacks. We analyze the vulnerabilities these systems face and propose advanced algorithmic solutions to fortify them against such risks. Moreover, this paper addresses the ethical and privacy concerns surrounding the widespread use of biometric data, emphasizing the need for stringent data protection measures and regulatory compliance. Additionally, the research investigates AI's significant contributions to genetic engineering, particularly through advancements in CRISPR [1] technology. By integrating AI, the precision of gene editing can be significantly improved, potentially revolutionizing personalized medicine and genetic therapies. This extensive research intends to shed light on the revolutionary potential of artificial intelligence (AI) in genetic engineering and biometric security, emphasizing both the exciting developments and the difficult obstacles still to be overcome. Through this research, readers will get a clearer knowledge of how artificial intelligence (AI) is altering biotechnology and security, opening the door for discoveries that might have a significant influence on healthcare and other fields.

Keywords: AI, Biometric Security, Facial Recognition, Machine Learning, Privacy Concerns.

DOI: https://doi.org/10.38124/ijisrt/IJISRT24JUN1510

PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24JUN1510.pdf

REFERENCES

  1. Garvie, C., Bedoya, A. M., & Frankle, J. (2016). "The Perpetual Line-Up: Unregulated Police Face Recognition in America." Georgetown Law Center on Privacy & Technology https://www. perpetuallineup.org/
  2. HSBC Press Release. (2018). "HSBC Introduces Fingerprint and Voice ID Security." Retrieved from https://www.hsbc. com/news-and-media
  3. Amazon Developer Blog. (2019). "Alexa Voice Profiles: Recognize Users and Personalize Experiences." https://developer.amazon.com/blogs/ alexa/post/9d45b5e3-2398-4ad7-8887-7684e63b0039 /alexa-voice-profiles-recognize-users-and-personalizeb-experi ences
  4. Parkhi, O. M., Vedaldi, A., & Zisserman, A. (2015). "Deep face recognition." British Machine Vision Conference. Retrieved from https://www.robots.ox. ac.uk/~vgg/publications/2015/Parkhi15/
  5. Graves, A., Mohamed, A.-r., & Hinton, G. (2013). "Speech recognition with deep recurrent neural networks." IEEE International Conference on Acoustics, Speech. https://ieeexplore.ieee.org/ document/6638947
  6. M. O. Ozcan, F. Odaci and I. Ari, "Remote Debugging for Containerized Applications in Edge Computing Environments," 2019 IEEE International Conference on Edge Computing (EDGE), Milan, Italy, 2019, pp. 30-32, doi: 10.1109/EDGE.2019. 00021.
  7. Rattani, A., Kisku, D. R., Bicego, M., & Tistarelli, M. (2010). "Feature Level Fusion of Face and Fingerprint Biometrics." IEEE International Conference on Biometrics: Theory Applications and Systems. https://ieeexplore.ieee.org/document/ 5634524