A survey of deep learning for face presentation attack detection
Mohammadreza Sheikhfathollahi, Simon Parkinson, Richard Hill, Saad Khan · Neurocomputing · 2025
Face anti-spoofing detection (FASD) has become a crucial technology due to the alarming advancements in presentation attacks (PAs). As more novel PAs with realistic generative capabilities emerge, improved biometric security solutions are needed to address these evolving threats. In early and foundational work, FASD techniques focused mainly on handcrafted features that were unreliable due to their limited representation capacity. In the recent decade, with the advancements in deep learning and its capabilities for image processing tasks and the emergence of large datasets, improvements in the performance of detecting PAs have been achieved. However, as research progresses rapidly, there is an absence of a comprehensive analysis of detection methods to understand the strengths and weaknesses of different types of approaches. Although various approaches utilise sensors in addition to RGB cameras, in this paper, we focus specifically on RGB camera-based methods and provide a comprehensive review of deep learning-based FASDs, including generalised models developed to date. In addition, the datasets are presented, along with the evaluation protocols and metrics. • This article provides an extensive analysis of deep learning-based approaches in the spatial domain, along with a focused review of frequency domain generalisation methods. • This paper covers the application of GANs for FASD, together with semi-supervised and self-supervised learning methods. Therefore, it provides the reader with state-of-the-art methods for different application scenarios (e.g., unseen domain generalisation and unknown attack detection). • The taxonomy of deep learning-based FASD methods using RGB cameras provides readers with an overview of modern approaches. • The strengths and weaknesses of existing models have also been explored and summarised, providing an overview of their capabilities and limitations.