{"id":203952,"date":"2021-03-17T00:29:51","date_gmt":"2021-03-16T21:29:51","guid":{"rendered":"https:\/\/en.buradabiliyorum.com\/new-biometric-system-identifies-users-by-analyzing-their-facial-movements\/"},"modified":"2021-03-17T00:29:51","modified_gmt":"2021-03-16T21:29:51","slug":"new-biometric-system-identifies-users-by-analyzing-their-facial-movements","status":"publish","type":"post","link":"https:\/\/buradabiliyorum.com\/en\/new-biometric-system-identifies-users-by-analyzing-their-facial-movements\/","title":{"rendered":"#New biometric system identifies users by analyzing their facial movements"},"content":{"rendered":"

“#New biometric system identifies users by analyzing their facial movements<\/strong>”
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\n Facial and fingerprint recognition are certainly convenient ways to unlock phones,\u00a0but they can sometimes be fooled by photos<\/a> or pointing handsets at sleeping users<\/a>.<\/p>\n

A new AI system aims to make\u00a0biometric authentication more secure by\u00a0analyzing\u00a0facial movements.<\/p>\n

The tech requires users to record a short\u00a0video of them making a unique facial motion.\u00a0An integrated neural network framework then inspects the footage to learn their features and movements concurrently. <\/span><\/p>\n

When the user later attempts to access their phone,\u00a0the system checks that their face\u00a0matches with the recorded data.<\/span><\/p>\n

The tech, called\u00a0Concurrent Two-Factor Identity Verification (C2FIV), was developed by Brigham Young University professor\u00a0D.J. Lee. He said<\/a> it could provide a safer\u00a0verification method than current\u00a0biometric identifiers:<\/p>\n

The biggest problem we are trying to solve is to make sure the identity verification process is intentional. 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. You see this a lot in the movies<\/a> \u2014 think of Ethan Hunt in Mission Impossible even using masks to replicate someone else\u2019s face.<\/p>\n<\/blockquote>\n

[Read:\u00a0How do you build a\u00a0pet<\/span>-friendly gadget? We asked experts and animal owners]<\/em><\/p>\n

In a preliminary study, Lee trained\u00a0the neural network on 8,000 clips of 50 subjects making various facial movements such as smiling, blinking, and raising their eyebrows. He said the system verified the identifies with over 90% accuracy \u2014 which could improve further with a larger training dataset.<\/p>\n

While modern facial\u00a0 unlock tech used in modern phones is hard to bypass with these tricks.<\/p>\n

The tech isn\u2019t likely to replace the face unlock systems used in modern smartphones, which are getting much harder to fool. But Lee believes\u00a0C2FIV could be used in a wide\u00a0range of app<\/a>lications, from online banking to vehicle access:<\/p>\n

We could build this very tiny device with a camera on it and this device could be deployed easily at so many different locations. How great would it be to know that even if you lost your car key, no one can steal your vehicle because they don\u2019t know your secret facial action?<\/p>\n<\/blockquote>\n

HT \u2013 Engadget<\/a>.<\/em><\/p>\n

\n Published March 16, 2021 \u2014 21:29 UTC<\/p><\/div>\n