Brigham Young University electrical and computer engineering professor D.J. Lee has developed a new algorithm which makes ID verification more secure by tracking facial motion as well as recognising faces.

The algorithm is called Concurrent 2-Factor Identity Verification (C2FIV) and requires both a facial identity and a specific facial motion to gain access. Operationally, a user faces a camera and records a short 1-2 second video of either a unique facial motion or the lip movement of a secret phrase. The video is then input into the device, which extracts facial features and the features of the facial motion, storing them for later ID verification.

“The biggest problem we are trying to solve is to make sure the identity verification process is intentional,” said Lee, a professor of electrical and computer engineering at BYU.

“If someone is unconscious, you can still use their finger to unlock a phone and get access to their device or you can scan their retina.”

2FIV relies on an integrated neural network framework to learn facial features and actions concurrently. This framework models dynamic, sequential data like facial motions, where all the frames in a recording must be considered.

Using this integrated neural network framework, the user’s facial features and movements are embedded and stored on a server or in an embedded device and when they later attempt to gain access, the computer compares the newly generated embedding to the stored one. That user’s ID is verified if the new and stored embeddings match at a certain threshold.

“We’re pretty excited with the technology because it’s pretty unique to add another level of protection that doesn’t cause more trouble for the user,” Lee said.

In their preliminary study, Lee and his Ph.D. student Zheng Sun recorded 8000 video clips from 50 subjects making facial movements such as blinking, dropping their jaw, smiling, or raising their eyebrows as well as many random facial motions to train the neural network.

They then created a dataset of positive and negative pairs of facial motions and inputted higher scores for the positive pairs (those that matched). Currently, with the small dataset, the trained neural network verifies identities with over 90 per cent accuracy. They are confident the accuracy can be much higher with a larger dataset and improvements on the network.

According to Lee, C2FIV has broad applications, including accessing restricted areas at a workplace, online banking, ATM use, safe deposit box access or as a hotel room entry or keyless entry/access to vehicles.

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