Hardware and Software Approaches to Fingerprint Liveness Detection: A Comparative Review

Despite being widely utilized, fingerprint recognition systems can be compromised by spoof attacks. Fingerprint liveness detection (FLD) techniques aim to differentiate between genuine and false fingerprints. This paper offers a thorough overview of these methods. In addition to discussing the difficulties presented by sophisticated spoofing materials and techniques, we explore several kinds of spoof attacks, including direct and indirect approaches. FLD methods that are based on hardware and software are examined in this paper, along with their advantages and disadvantages. Strong security is provided by hardware-based methods, such as those that use pressure, scent, electrical, or temperature sensors; however, they frequently need extra hardware parts. In contrast, software-based methods check fingerprint images for liveness cues including texture, color, and motion by employing different algorithms of image processing and pattern recognition. The purpose of this study is to review the most recent FLD research to shed light on the field's present trends and potential prospects. We wrap up by talking about the issues that still need to be resolved and possible directions for further studies, like creating more reliable and effective FLD methods that can adjust to changing spoof assaults. Additionally, we touch on some types of datasets specifically designed for the development of FLD algorithms, as well as the metrics utilized to assess the performance of these algorithms to classify real and spoof fingerprints