Facial recognition access control technology has become increasingly sophisticated in our country and is now widely integrated into our daily lives. Its sensorless, intelligent, and rapid capabilities have garnered significant favor among users. To prevent malicious hacking and ensure security, facial anti-spoofing technology for live body detection has become an essential part of this system. Live body detection methods are highly secure, but they often require specific actions from users, which can impact the overall user experience. For instance, some sensorless systems, like face recognition access control devices, rely on image analysis and light effects to differentiate between live faces and fakes.
One common approach involves using ordinary cameras for live detection. Even without requiring specific actions, slight movements or micro-expressions, such as subtle eyelid rhythms, eye movements, or lip activity, can help identify a live subject. By analyzing physical features—either individually or in combination—we can train neural network classifiers via deep learning to discern between live and fake faces. These physical features typically include texture, color, spectral properties, motion, image quality, and even heart rate data.
Another method employs infrared cameras. Infrared-based live detection primarily relies on the optical flow technique, which tracks pixel intensity changes over time to detect motion. By applying Gaussian differential filters, LBP (Local Binary Patterns), and support vector machines, the system can analyze data statistically. This method is particularly effective because it’s less reliant on user cooperation, making it suitable for blind tests.
A third option involves 3D cameras. These capture detailed 3D data of the face, from which distinctive features are selected to train neural network classifiers. Feature selection is critical here, balancing both global and local information to ensure stable and robust algorithms. The process generally includes three stages: first, extracting and analyzing the geometric relationships of key points in live versus non-live faces; second, processing the full-face area to train classifiers on positive and negative samples; finally, fitting surfaces to describe 3D model features. Raised areas are identified based on surface curvature, and EGI (Extended Gaussian Images) features are extracted for each region before using spherical correlation for final classification.
Despite its growing popularity across industries, providing users with smarter and faster functionalities, facial recognition access control must prioritize safety. As this technology becomes more ubiquitous, balancing usability with security remains paramount.
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