Learning From Easy to Hard: Fingerprint Spoof Detection With Hard Sample Mining

Fingerprint authentication systems can be tricked by presentation attacks (using fake fingerprints to gain unauthorized access), and while deep learning methods help detect these spoofs, they struggle because different fake materials look different and the AI focuses on easy cases while missing hard ones. This paper proposes EasyHard-FSD, a method that uses hard sample mining (a technique where an AI identifies the trickiest examples to learn from) with a teacher/student model setup to help the AI better distinguish real fingerprints from fake ones.