Finger vein liveness detection using motion magnification

Finger vein recognition has emerged as an accurate and reliable biometric modality that was deployed in various security applications. However, the use of finger vein recognition also indicated its vulnerability to presentation attacks (or direct attacks). In this work, we present a novel algorithm to identify the liveness of the finger vein characteristic that is presented to the sensor. The core idea of the proposed approach is to magnify the blood flow through the finger vein to measure its liveness. To this extent, we employ the Eulerian Video Magnification (EVM) approach to enhance the motion of the blood in the recorded finger vein video. Next, we further process the magnified video to extract the motion-based features using optical flow to identify the finger vein artefacts. Extensive experiments are carried out on a relatively large database that is comprised of normal presentations vein videos from 300 unique finger instances corresponding to 100 subjects. The finger vein artefact database is captured by printing 300 real (or normal) presentation image of the finger vein sample on a high-quality paper using two different kinds of printers namely laser and inkjet. Extensive comparative evaluation with four different well-established state-of-the-art schemes demonstrated the efficacy of the proposed scheme.