Finger vein liveness detection using motion magnification

Abstract

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 enhancing 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 300 unique finger vein videos corresponding to 100 subjects. The finger vein artefact database is captured by printing the real (or normal) presentation image of the finger vein 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.

Biometric Presentation Attack Detection: Beyond the Visible Spectrum

“…The initial approaches to PAD were based on the socalled handcrafted features, such as texture descriptors or motion analysis [6], [18]. However, in the last years deep learning (DL) has become a thriving topic [19]- [21], and biometric recognition in general, and PAD in particular, are not an exception.…”

State of the Art in Vascular Biometrics

“…average speckle intensity). The technique proposed by [218] aims also at liveness detection and relies on LED-NIR video data. In this approach, motion magnification is employed to magnify the subtle motion of finger veins caused by blood flow.…”

Presentation Attack Detection for Finger Recognition

“…Also in 2015, Raghavendra et al [60] analysed short video sequences with the aid of Eulerian video magnification [78]. The goal was to amplify the blood flow and thus detect the printed artefacts.…”

“…Two years later, the first competition on finger vein PAD was organised [75], where three different teams participated. Since then, different PAD approaches have been presented, based on either a video sequence and motion magnification [60], texture analysis [44,61,71], image quality metrics [7], or more recently, neural networks [52,59,63] and image decomposition [58].…”