  Face Recognition (FR) systems are being used in a variety of applications,
including road crossings, banking, and mobile banking. The widespread use of FR
systems has raised concerns about the safety of face biometrics against
spoofing attacks, which use the use of a photo or video of a legitimate user's
face to gain illegal access to the resources or activities. Despite the
development of several FAS or liveness detection methods (which determine
whether a face is live or spoofed at the time of acquisition), the problem
remains unsolved due to the difficulty of identifying discrimination and
operationally reasonably priced spoof characteristics but also approaches.
Additionally, certain facial portions are frequently repeated or correlate to
image clutter, resulting in poor performance overall. This research proposes a
face-anti-spoofing neural network model that outperforms existing models and
has an efficiency of 0.89 percent.
