AI-Enabled Smart Surveillance System for Anti-Spoofing Using Raspberry Pi, Dashboard, and Simulink
Keywords:
CNN, Deep learning, Edge computing, Face anti-spoofing, Raspberry Pi, Simulink, Smart surveillance, YOLOAbstract
Facial recognition is now embedded in a wide range of applications, from unlocking personal devices to guarding sensitive infrastructure, which has pushed anti-spoofing research to the front of the security agenda. Conventional surveillance pipelines frequently fall short once an attacker moves beyond a simple printed photo to more advanced presentation attacks, leaving a real security gap. This review looks at how artificial intelligence can be paired with low-cost embedded hardware, specifically Raspberry Pi and MATLAB Simulink, to build surveillance systems that resist spoofing while running at the edge. Attention is given to techniques that exploit edge computing, deep architectures such as convolutional neural networks (CNNs) and You Only Look Once (YOLO), and multi-modal sensing, all aimed at raising both the accuracy and the real-time responsiveness of face anti-spoofing (FAS). The strengths and shortcomings of current methods are weighed against one another, along with how well each copes with 2D print attacks, replayed video, and 3D masks. The review also surfaces the open research problems that remain, including cross-dataset generalization and the computational limits of resource-constrained hardware, with the aim of pointing researchers and practitioners toward more secure, efficient, and scalable AI-enabled surveillance in the future.
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