If you’re an EU citizen and you’ve taken a trip abroad recently you may have experienced the relative ease of passing border control via an ePassport gate. These systems rely on face recognition technology in order to verify that you are carrying an authentic and valid passport. However, the setting for this technology is highly constrained; requiring a well-lit expressionless frontal image of the individual.
The gates are also being carefully monitored by a number of security personal who ensure that no one attempts to tamper with the authentication process. In contrast, e-assessment systems are often accessed from the comfort of a student’s home and therefore represent a much more challenging scenario for authentication.
Thankfully, recent advances in face recognition technology have enabled automated systems to perform face recognition under extremely challenging scenarios. In contrast to the more traditional biometric methods commonly employed by systems such as the ePassport gates, modern face recognition makes use of the millions of images now available through the Internet.
For example, a recent work by Facebook, which they named DeepFace, achieved facial recognition rates that are within 0.15% of humans with respect to mean accuracy. This impressive performance marks a shift in the availability of extremely accurate facial recognition systems under challenging scenarios, such as e-assessment.
The TeSLA system will leverage these new technologies in order to provide state-of-the-art face recognition systems for e-assessment purposes. The TeSLA system has been designed from the ground up with privacy in mind and students can be assured that they are being robustly verified by TeSLA without any risk of their privacy being violated.
Patrick Snape, Imperial and TeSLA project contributor
FUNDED BY THE EUROPEAN UNION
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