Anti-spoofing detection on facial imaging database with contrast and edge enhancement preprocessing steps
Neetika Gupta, Amandeep Kaur · Computational Methods in Science and Technology · 2024
Modern biometric systems need user authentication. Face presentation attacks employ FD camera videos or fake faces to gain unwanted access. Privacy needs strong face authentication and anti-spoofing. Face spoofing detection uses contrast augmentation, contrast development, and edge-based preprocessing. These preprocessing approaches increase face traits and picture quality. DoG and median filters reduce noise and normalize RGB images. Histogram equalization and Gabor filter contrast faces. This paper explains the pros and cons of dataset-based performance metrics like HTER and EER. Filters greatly improve research processing. Replay-Attack, 3DMAD, and NUAA validate the study model. The study model features 0.39% NUAA HTER and 0.4% replay attack HTER.