Hardware- and software-based fingerprint liveness detection schemes—A literature review

Technological advancements have shaped the methods for ensuring personal safety and safeguarding digital assets. In this regard, biometric systems are innovative technological solutions that utilize various behavioral and physiological traits for personal verification and identification. This technology provides efficient and seamless authentication while improving users' convenience in a range of security applications, including border security, physical access control, healthcare, and financial transactions. Fingerprint-based verification systems are vulnerable to various attacks, including spoofing, presentation attacks, and latent fingerprint attacks. Henceforth, it is essential to incorporate a robust and reliable liveness detection mechanism into the authentication system to enhance the security of such systems. Since fingerprint liveness detection has been an active area of research, many techniques have been proposed for handling this problem by extracting different characteristics such as minutiae, ridges, texture, pores, perspiration, and morphological features. In this article, the state-of-the-art liveness detection schemes along with their taxonomy from 2003 to 2025 are chronologically analyzed. In this article, we have presented the techniques, their parameters, their pros and cons, and the evaluation matrices. Moreover, this article inculcates a comprehensive survey of the various hardware-, software-based approaches focusing on the deep learning liveness detection techniques. We have also investigated the standard benchmarks of the LivDet series that are publicly available and the evaluation protocols.