Liveness Detection in Finger Vein Imaging Device Using Plethysmographic Signals

Abstract

Finger vein modality is a relatively new area in biometrics that overcomes the limitations of biometric systems based on external features. Despite the fact that finger veins are invisible to naked eye and latent print doesn’t exist, presentation attack on finger veins is possible if stored samples are stolen or compromised. To counter these attacks, liveness was ascertained using learning based methods. However, these methods are designed to detect only finger vein artefact generated using specific materials. Hardware based liveness detection methods make use of intrinsic characteristics of a live body to differentiate living tissues from artificially created materials resembling it. Thus hardware based liveness detection methods appear to be more robust to a wider class of spoofing attacks. In this paper, we propose a finger vein biometric device with a switchblade model sensor plate to ascertain the presence of a live finger. The blood flow pattern obtained from the sensor is hard to replicate and the presence of a physiological signal inherently implies liveness of the subject. The results after comparing quality of the vein images acquired from the proposed device and images from open databases show that the proposed device produces good quality images. The experimental results demonstrate that the developed prototype device with presentation attack detection (PAD) can successfully avert spoof attacks.