Spoof detection based on score fusion using ensemble networks robust against adversarial attacks of fake finger-vein images

Abstract

Finger-vein recognition is a biometric technology, which makes use of the pattern of veins in the skin of fingers. Finger-vein recognition is widely employed owing to its high recognition rate and ease of use. A spoof detection method is essential for identifying spoof attacks in biometric-recognition systems. Studies have primarily investigated methods for detecting spoof attacks that utilize image printing or replay using fake finger-vein images. However, no studies have investigated the detection of spoof attacks based on fake finger-vein images generated through a generative adversarial network (GAN), which is commonly implemented in deep-learning technologies. To solve this problem, we newly propose a spoof attack detection method robust against GAN-based fake finger-vein image generation. We verified the use of spoof finger-vein images generated through cycle-consistent adversarial networks (CycleGAN) to attack conventional finger-vein recognition systems and used this finding as the motivation for our research. We also propose a finger-vein spoof-detection method that employs score fusion of ensemble networks based on support vector machine using adaptive moment estimation (Adam) with a sharpness-aware minimization (SAM) optimizer. We conducted experiments using two open databases, ISPR and Idiap, to evaluate the proposed framework. The average classification error rates (ACER) of spoof detection using the proposed method with ISPR and Idiap were 0.37% and 0.23%, respectively. This result confirms the better performance by our method than conventional state-of-the-art methods.