Facial recognition access control technology has grown increasingly sophisticated in our country and is now widely integrated into our daily lives. Its sensorless, intelligent, and rapid capabilities have earned it significant favor among users. However, ensuring security remains paramount, which is why live body detection technologies have become essential to prevent malicious hacking attempts. These methods are highly secure, but requiring specific user actions can sometimes detract from the overall user experience. To address this, some devices, like certain face recognition access control systems, rely on image analysis and lighting effects to differentiate between real faces and artificial ones.
One approach involves using standard cameras for live detection. Even without explicit user instructions, slight movements and micro-expressions—such as the rhythm of eyelid blinking, subtle lip movements, or the dynamic interaction between facial features—are enough to identify a live person. By analyzing physical traits like texture, color, spectral properties, motion, image quality, and even heart rate data, neural networks can be trained to distinguish between genuine faces and spoofed images.
Infrared cameras offer another method of live detection, primarily through the optical flow technique. This method tracks pixel-level motion across image sequences, detecting eye movements and blinks by analyzing time-domain changes. Since this technique doesn’t require active user participation, it provides an effective way to conduct blind tests.
Three-dimensional (3D) cameras take live detection a step further by capturing detailed 3D facial data. From this, both global and localized features are extracted to train neural networks capable of differentiating between live faces and masks. The process involves three key stages: first, extracting and analyzing geometric relationships among key facial points; second, processing the full-face region and coordinating positive and negative sample training; and third, fitting surfaces to describe 3D model features, extracting curvature-based regions, and applying spherical correlation for final classification.
While facial recognition access control systems are gaining popularity due to their efficiency and convenience, we must always prioritize security. As this technology continues to evolve, balancing innovation with safety will remain critical for its long-term success.
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