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Improving ATM Security via Facial Recognition

Improving ATM Security via Facial Recognition. CPSC510 James Maxlow November 25 th , 2002. Proposal. Cameras in use at automatic teller machines should take still images of users A facial recognition scheme should be added to the software used to verify users at ATMs

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Improving ATM Security via Facial Recognition

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  1. Improving ATM Securityvia Facial Recognition CPSC510 James Maxlow November 25th, 2002

  2. Proposal • Cameras in use at automatic teller machines should take still images of users • A facial recognition scheme should be added to the software used to verify users at ATMs • This scheme should match a picture of the user at the ATM with a picture of the account holder in the bank’s database 2 of 11

  3. Reasoning • ATM fraud costs U.S. banks an average of $15,000 each year… hundreds of millions in total • This cost is borne by bank customers • Current ATM validation schemes are limited to access cards and PINs • Card theft, PIN theft and cracking, stealing of account information by bank employees all contribute to fraud schemes 3 of 11

  4. Algorithm • Take customer’s picture(s) when account is opened and allow user to set non-verified transaction limits • At ATM, use access card and PIN to pre-verify user • Take user’s picture, attempt to match it to database image(s) • If match is successful, allow transaction • If match is unsuccessful, limit available transactions 4 of 11

  5. Can ATMs Support This? • Most current generation ATMs run Windows CE, 2000, XP Embedded, or Linux – these machines can run facial recognition software locally ATM Bank Computer 1: Image, account no, PIN 2: Account no, PIN 5: User verified 3: Bank-held customer image 4: Processing 5 of 11

  6. Can ATMs Support This? • Older ATMs run DOS or OS/2 – these machines can offload the processing to the bank’s computers ATM Bank Computer 2: Image, account no, PIN 1: Image, account no, PIN 5: User verified message 4: User verified message 3: Processing 6 of 11

  7. Is Facial Recognition Reliable? • Matching a user image with any image in a database… facial evaluation… is still problematic – digital airport screenings, public surveillance cameras, etc. have high error rates • But this isn’t a problem here! • We must only match a user image with one known image from a database… facial verification… this technique has low error rates under good conditions (3% - 10%) 7 of 11

  8. What Variables Affect Verification? • Lighting • Angle of view • Extreme facial expressions • Facial hair • Glasses • Please follow these guidelines to • help us verify your identity: • Face the camera, holding still until • you hear the beep • Maintain a normal facial expression • If you are wearing glasses, please • remove them 8 of 11

  9. What Is The Best Verification? • Local Feature Analysis • This method analyzes the geometric relationships between facial features • In some versions it builds a 3-D topography of the user’s face and uses this topography for later comparisons 9 of 11

  10. What Would I Actually Do? • Find open-source LFA recognition programs • Write an ATM black box module • Create two databases of images • Tweak and test recognition programs with ATM module and images • Rewrite ATM module into client/server version with encryption to emulate ATM/bank interactions • Add USB camera control to client 10 of 11

  11. Summary • Access card / PIN provides insufficient ATM security • Adding facial verification to the process can greatly decrease fraudulent transactions • Current ATMs have the power to perform verification locally given a software change • I will create a simulated system using a custom ATM black box and an open-source LFA recognition program 11 of 11

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