National defense technology refers to the vein identification: the false detection rate is as low as one in 100,000[Full text]

Recently, the digital video research group at the School of Electronic Science and Engineering, National University of Defense Technology, has spent over a decade conducting in-depth research and successfully developed a finger vein feature identification and authentication system. This advanced technology boasts an extremely low false acceptance rate—just one in 100,000—and has already secured five national invention patents, marking a breakthrough that breaks the previous dominance of countries like Japan and South Korea in this field. Currently, the team is finalizing product optimizations and plans to launch it commercially after the Spring Festival. During a visit to the National Defense University Laboratory, we had the opportunity to experience the vein recognition device firsthand. The process was simple and fast: placing your finger into the machine automatically registered your unique vein pattern within just 30 seconds. Then, when the same finger was placed back into the device, the system instantly verified the identity and unlocked a door—showcasing the speed and accuracy of the technology. According to Xie Jianbin, the project leader, vein recognition is a biometric technology that surpasses fingerprint scanning in terms of security and reliability. It works by capturing the unique patterns of blood vessels under specific lighting conditions, making it a true "live detection" method. Unlike static fingerprints, vein patterns are more difficult to replicate, offering higher security and broader application potential across various industries. In addition, the research team has been working on reducing the size of the device to enhance its usability. The reporter observed that the entire system stands only about 6 centimeters tall, similar in size to a computer mouse, making it highly portable and convenient for real-world deployment. Xie Jianbin also mentioned that the next phase of research will focus on two key areas. First, improving the system's ability to detect and extract vein features even under low-visibility conditions, thereby enhancing image clarity and detail. Second, expanding the system’s capability to recognize non-rigid targets, such as fingers that are bent or rotated, which will significantly improve its adaptability and performance in real-life scenarios.

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