Secure Biometric Signal Processing; A Review

Abstract

In recent year, biometric technologies have become an integral part of modern authentication systems, offering distinct advantages such as convenience, uniqueness, and resistance to credential theft. However, as these systems are increasingly deployed in critical applications ranging from border control and e-governance to mobile security and financial transactions the security and privacy of biometric signal processing have emerged as paramount concerns. This study discussed the critical challenges faced by biometric systems, focusing on recognition accuracy, scalability, and ethical implications related to surveillance and consent. The study examines advanced security techniques, including cancelable biometrics, steganography and watermarking, homomorphic encryption, highlighting their potential to enhance the security of biometric data. Additionally, the integration of multimodal biometrics is discussed as a strategy to improve resilience against emerging threats like sensor attacks and template inversion. The study emphasizes the necessity of adopting privacy-by-design principles to mitigate unauthorized access and data breaches, ensuring user control over biometric information. Further, the findings underscore the importance of developing robust defense mechanisms that adapt to evolving security threats while preserving user privacy. By synthesizing recent advancements in secure biometric signal processing, this paper provides a comprehensive framework for enhancing security, usability, and efficiency in future biometric applications.