  Fingerprint recognition systems are widely deployed in various real-life
applications as they have achieved high accuracy. The widely used applications
include border control, automated teller machine (ATM), and attendance
monitoring systems. However, these critical systems are prone to spoofing
attacks (a.k.a presentation attacks (PA)). PA for fingerprint can be performed
by presenting gummy fingers made from different materials such as silicone,
gelatine, play-doh, ecoflex, 2D printed paper, 3D printed material, or latex.
Biometrics Researchers have developed Presentation Attack Detection (PAD)
methods as a countermeasure to PA. PAD is usually done by training a machine
learning classifier for known attacks for a given dataset, and they achieve
high accuracy in this task. However, generalizing to unknown attacks is an
essential problem from applicability to real-world systems, mainly because
attacks cannot be exhaustively listed in advance. In this survey paper, we
present a comprehensive survey on existing PAD algorithms for fingerprint
recognition systems, specifically from the standpoint of detecting unknown PAD.
We categorize PAD algorithms, point out their advantages/disadvantages, and
future directions for this area.
