MLbFA: A Machine Learning-Based Face Anti- Spoofing Detection Framework under Replay Attack

Abstract

The primary aim of the research paper is to deploy an efficient automated face antispoofing system that could consider replay attacks in the presence of partial occlusions. For this purpose, the article introduces a novel machine learning-based face- antispoofing (MLbFA) framework. The system incorporates a modified version of the difference of the Gaussian technique to compute the overall contrast of the input images which is later used to enhance the contrast of the image using contrast correction. On the other hand, the image details, especially the edges are enhanced for significant feature contribution using a Beltrami filter. The contrast-cured and extremity-enhanced images are averaged to obtain a finer image. Face cropping is achieved using the Bounding- Box algorithm to reduce computational complexity and improve classification accuracy for region-bounded feature extraction. Quality conventional or handcrafted features (CF/HF) are extracted through various descriptors from the region of interest (ROI). The features are reduced in dimension using principal component analysis (PCA) and portioned in training and testing sets with a 75%:25% ratio respectively. An experimental study showed that the proposed MLbFA model using a Support Vector Machine (SVM) outperforms other recent existing face anti-spoofing competing techniques with an improvement of 0.11% compared to the best- performing Edge-Net Autoencoder model concerning the classification accuracy.