We've updated our Privacy and Cookies Policy
We've made some important changes to our Privacy and Cookies Policy and we want you to know what this means for you and your data.
Chinese uni uses facial recognition to track absent students
- Author, News from Elsewhere...
- Role, ...as found by 成人快手 Monitoring
A Chinese university is using facial recognition technology to deter students from bunking off, it's reported.
According to , students in six classes at the Communications University of China in the city of Beijing are being told to stand in front of an interactive screen when they arrive for lectures.
They have their photos taken, which are then matched against those in the university's database within a couple of seconds. The device is highly accurate at identifying students from their appearance, even if they change their hair or wear makeup.
Professor Shen Hao says it has proven effective in helping lecturers to identify students who regularly skip classes.
"The new system saves time and reduces the workload of teachers," he adds, noting that the system is a shift from from the traditional paper registers used in Chinese universities.
One student in Mr Shen's class tells Beijing News that it's a "novel idea", and another says: "It's quite convenient; our teacher doesn't need to do a register."
But there is some concern online about the introduction of such technology into schools.
"This is just so鈥 weird, I hope our school doesn't copy it," says one user of the popular Weibo microblog. Many online commentators say they feel lucky that they have already graduated.
This is not the first time that the use of facial recognition technology at a public institution has sparked an online outcry in China.
In March, Weibo users criticised the Temple of Heaven tourist site in Beijing for installing facial recognition at the entrance to public toilets in order to stop visitors from stealing toilet paper.
Reporting by Kerry Allen
Use #NewsfromElsewhere to stay up-to-date with our reports via .
Top Stories
More to explore
Most read
Content is not available