AI-Driven Face Spoof Detection: A Comprehensive Analysis of Machine Learning and Deep Learning Approaches

Abstract

With the increasing reliance on facial recognition systems in security and authentication applications, face spoof detection has become a critical area of research. Traditional handcrafted feature-based methods have evolved into AI-driven approaches that leverage machine learning (ML) and deep learning (DL) techniques to enhance detection accuracy. This paper presents a comprehensive review of various AI-based face spoof detection techniques, including Support Vector Machines (SVM), Decision Trees, Random Forest (RF), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). The study explores feature extraction methods such as Local Binary Patterns (LBP), chromatic movement analysis, and reflection detection, evaluating their contributions to spoof detection accuracy. Additionally, challenges such as dataset bias, adversarial attacks, computational efficiency, and generalization across diverse spoofing techniques are discussed. The paper further highlights recent advancements in hybrid AI models, real-time deployment strategies, and multimodal biometric authentication. The findings underscore the importance of optimizing feature selection, enhancing model robustness, and improving generalization to strengthen biometric security systems. Future research directions emphasize lightweight AI architectures, explainable AI (XAI), and adversarial defense mechanisms for next-generation face spoof detection systems.