The campus is the second home for the students, which guarantees the safety of the campus to ensure the safety of the students. For the campus security, it is very important to establish a sound campus security risk prevention and control system. Cardlan Tech deploys the face recognition technology on the campus, using the power of technology to provide convenience for students, provides a scientific and rational wisdom management for school management, and pushes the smart campus to a new level.
Cardlan Tech face recognition technology can be used in campus entrances and exits, dormitory management, library entrances, teaching building attendance and other places, campus people are too many to manage, these places are usually the places where students often go in and out. It often takes a lot of manpower and material resources in the school security management. The correct use of the face recognition system will save a lot of manpower and material resources for school management, and at the same time makes management more efficient and intelligent.
Before using face recognition, input the student's name and photo information on the system in advance. Students do not need to swipe the campus card or present the student ID when entering or leaving the entrance. Just look at the face scanner and you can quickly collect the students’ face image. The face image is compared with the personal photo information registered in advance. As long as the live photo matches the registered photo, the access control will be automatically released. If an outside person breaks in, the system will automatically alarm to remind the security personnel to go forward. The burden on campus security personnel has been alleviated in somehow.
The face recognition access control system facilitates the travel of teachers and students, brings intelligent experience to teachers and students, and prevents the random entry and exit of outsiders, making the campus environment more secure and stable.
The accuracy of face recognition from Cardlan Tech can be as high as 99.5%. At the same time, the algorithm research is carried out on the problem that the daytime glare is too bright and the night light is too weak, which can realize accurate face recognition in daytime, evening and cloudy days.
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