Year: 2026
Type: article
Abstract: The vulnerabilities of the fingerprint authentication system have raised security concerns in terms of adapting them in highly secured access control applications. Therefore, fingerprint presentation attack detection (FPAD) methods are essential to ensure reliable fingerprint authentication. Due to the lack of generalization of the traditional handcrafted-based approaches, deep learning-based FPAD has become mainstream and achieves remarkable performance in the past decade. In this paper, we will concentrate only on deep learning-based FPAD methods. We investigate recent methods and divide those into different categories to provide a comprehensive description. The benchmark metrics and publicly available datasets are also discussed. Lastly, we conclude the paper by discussing future perspectives to inspire further research.
Source: Sensors
DOI: 10.3390/s26041283
Authors: Hailin Li, Raghavendra Ramachandra
Institution: Norwegian University of Science and Technology
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
Funder: Norges Forskningsråd
Award: G4802910238
OpenAlex ID: W7129073669