You can work on biometric data security. Review previous work, and Find out the limitations of previous research. discover and research technical aspects of the biometrics system, and consider the biometrics data's security. Basically, it depends on your research methodology and what problem you want to resolve.
Dear Rahila N Ameer. One area of research that appears to be very interesting and novel is the application of Delaunay triangulation or Vornonoi polygons to fingerprint identification. I suggest you consult: Bebis, G., Deaconu, T., & Georgiopoulos, M. (1999, October). Fingerprint identification using delaunay triangulation. In Proceedings 1999 International Conference on Information Intelligence and Systems (Cat. No. PR00446) (pp. 452-459). IEEE. https://link.springer.com/book/10.1007/978-3-030-83624-5#toc
Some applications of the Delaunay triangulation in "Graphical Authentication" can be consulted at: "Weak PassPoint Passwords Detected by the Perimeter of Delaunay Triangles," Security and Communication Networks, vol. 2022, Article ID 3624587, 14 pages, 2022. https://doi.org/10.1155/2022/3624587 and (2022). Weak PassPoint Passwords Detected by the Perimeter of Delaunay Triangles. Security and Communication Networks, 2022. https://doi.org/10.3390/s22051987
In case you are interested in the topic, the mentioned reference, Bebis, G., Deaconu, T., & Georgiopoulos, M. (1999, October), is just an example and starting point, since there is more recent work on this topic than you could consult.
Biometric security is a rapidly evolving field with various working areas and ongoing research. Some of the current focus areas within biometric security include:
Multi-Modal Biometrics: Combining multiple biometric traits (e.g., fingerprints, facial recognition, iris scans) for improved accuracy and robustness.
Deep Learning and AI: Utilizing advanced machine learning techniques, such as deep neural networks, to enhance biometric feature extraction, matching, and anti-spoofing.
Anti-Spoofing Techniques: Developing methods to detect and prevent spoofing attacks using fake biometric data, like printed photos or silicone replicas.
Continuous Authentication: Implementing systems that continuously verify the user's identity during a session to enhance security.
Mobile Biometrics: Integrating biometric authentication into mobile devices for secure access to apps, payments, and data.
Privacy-Preserving Biometrics: Designing systems that protect user privacy by storing biometric templates in encrypted or transformed forms.
Biometric Template Protection: Employing techniques to securely store and transmit biometric templates to prevent template leakage.
Behavioral Biometrics: Analyzing user behaviors, like typing patterns or gait, to authenticate individuals based on unique behavioral traits.
Biometric Encryption: Developing methods to encrypt data using biometric information as a key, enhancing data security.
Fusion with Other Technologies: Integrating biometrics with other security measures like two-factor authentication, tokenization, and cryptography.
Scalability and Usability: Ensuring biometric systems are user-friendly and capable of handling large user populations.
Ethical Considerations: Addressing ethical issues related to biometric data usage, storage, and potential biases.
Standardization: Developing industry standards to ensure interoperability, security, and consistency in biometric systems.
Cross-Domain Applications: Applying biometric technology beyond traditional security, such as healthcare, finance, and smart homes.
Liveness Detection: Enhancing systems to distinguish between live individuals and replayed biometric data.
Robustness to Environmental Factors: Creating systems that work reliably in various environmental conditions, such as poor lighting or noisy backgrounds.
Mobile and Wearable Biometrics: Exploring biometric authentication on wearable devices like smartwatches and fitness trackers.