Comparison of Face Spoof Prediction Model efficiencies using Machine Learning Algorithms

Abstract

With the rise of facial recognition technologies, face spoof detection is crucial. Protecting the privacy and safety of these increasingly vital technologies requires this. Face spoofing attacks, in which attackers use fake or altered facial data to mislead face recognition systems, undermine its “accuracy” and trustworthiness. We provide a detailed machine learning research on optimizing face spoof prediction algorithms.

This research collects data, cleans it, creates features to evaluate, chooses a machine learning method, and tests it. A well-maintained pool of actual and artificial face samples covers many assault circumstances. Various feature engineering methodologies are examined to enhance model discrimination. Local Binary Patterns (LBP), Histogram of Oriented Gradients (HOG), and pre-trained VGG-16 model "deep learning-based feature extraction" are examples.

We use “Support Vector Machines (SVM)”, “Random Forest”, and “Convolutional Neural Networks (CNN)” to predict face spoofs. The CNN-based algorithm compares genuine and artificial faces with high “accuracy”, “precision”, “recall”, “F1 score”, and “ROC curve (AUC-ROC)”.

Research has wide-ranging effects. The recommended solution prevents complicated spoofing attacks and improves facial recognition systems. A seamless user experience is achieved by reducing erroneous rejections and improving user acceptance. The model's adaptability to new spoofing threats ensures its longevity.

Findings enhance biometric authentication research and give wider solutions. The recommended approach is tested for bias and fairness to determine ethics. Future routes include large-scale deployment, hybrid techniques, privacy protection, fairness, and continual learning. The work provides crucial machine learning advice for improving face spoof prediction algorithms. By making face recognition systems safer and more trustworthy, this work advances technology and benefits companies, individuals, and the planet.