Presentation Attacks in Palmprint Recognition Systems

Abstract

Background: A presentation attack places the printed image or displayed video at the front of the sensor to deceive the biometric recognition system. Usually, presentation attackers steal a genuine user's biometric image and use it for presentation attack. In recent years, reconstruction attack and adversarial attack can generate high-quality fake images, and have high attack success rates. However, their attack rates degrade remarkably after image shooting. Methods: In order to comprehensively analyze the threat of presentation attack to palmprint recognition system, this paper makes six palmprint presentation attack datasets. The datasets were tested on texture coding-based recognition methods and deep learning-based recognition methods. Results and conclusion: The experimental results show that the presentation attack caused by the leakage of the original image has a high success rate and a great threat; while the success rates of reconstruction attack and adversarial attack decrease significantly.

Gesture Recognition and Hand Tracking for Anti-Counterfeit Palmvein Recognition

“…We build an interactive system, which can recognize hand gestures and track hands for palmvein recognition in infrared environments. Usually, illegal users try to steal genuine users' biometric images to impersonate their identities [13,14]. If the hand is required to make an "unpredictable" gesture, an illegal user cannot use a simple fake palmvein tool to pass the authentication test, because a simple tool cannot make a gesture like a real hand.…”