A Survey on Deep Learning for Face Anti-Spoofing in Automotive Applications

Facial recognition (FR) is increasingly deployed in vehicles for authentication and personalized user settings. However, these systems are vulnerable to sophisticated spoofing attacks, necessitating robust face anti-spoofing (FAS) solutions. Deep learning (DL)-based FAS has achieved strong results in general settings, but its adaptation to in-vehicle environments faces distinct challenges, including dynamic lighting, frequent occlusions, and limited onboard computational resources. Although there is extensive research on general FAS, its application and performance within in-vehicle environments remain underexplored. This paper reviews the state of the art DL-based FAS methods and evaluates their applicability to automotive contexts. The survey covers a detailed taxonomy of traditional and advanced approaches, including Convolutional Neural Networks (CNNs), Vision Transformers (ViT), generative models, multimodal and spatiotemporal frameworks, adversarially robust techniques, and data-efficient paradigms. The paper also outlines evaluation metrics, cross-dataset protocols, and publicly available datasets, highlighting the scarcity of realistic in-vehicle benchmarks. Finally, we discuss deployment challenges unique to vehicular settings and identify future directions such as domain-adaptive learning, synthetic and simulation-based data generation, and multimodal large language models. By bridging current methods with the specific demands of vehicles, this survey provides a foundation for developing secure, lightweight, and explainable FAS mechanisms suitable for deployment on intelligent vehicle systems.