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Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
Facial recognition
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Facial recognition

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  • -Age is an issue: a study by government’s National Insitute of Standards and Technology found false negative rates for face-recognition verification of 43 percent using photos from only 18 months earlier
  • Skin biometrics: surface texture analysis of texture of skin, uses algorithms to turn patch of skin into mathematical, measurable space Can define the differences between twins Many problems wouldn’t work if: significant glare on sunglasses, long hair in center of face, poor lighting, lack of resolution
  • detection: scans already existing 2d photograph or a shot from a 3D video Alignment: up to 90 degrees turned hereMeasurement: measures curves of face on submillimeter scale and creates a templateRepresentation: translates into unique code to create template with set of numbers to represent a person’s faceMatching: new technology converts 3D image into 2D image using algorithm to compare to 2D image in database
  • From :55 to 2:05ish
  • Transcript

    • 1. Christina Carr, Becky Schaffran, & Tess CiminiFACIAL RECOGNITION
    • 2. What is it? Facial recognition systems are built on computer programs that analyze images of human faces for the purpose of identifying them.
    • 3. How does it work? Measure specific facial characteristics to create unique file called “template” Using templates, compare image to another image  Produces a score on similarity  Video camera signals  Pre-existing photos  i.e. drivers license databases
    • 4. 2D Facial Recognition 2D Recognition  Maximum angle: 35 degrees  Must be similar to program in database  Sometimes ineffective due to lighting changes and other uncontrolled variables
    • 5. 3D Recognition 3D Recognition  Can create template from face at 90 degree angle  More accurate  Uses depth and an axis of measurement not affected by lighting Example: Identix® - FaceIt®  Landmarks or nodal points  And now: FaceIt®Argus, skin biometrics
    • 6. Steps of 3D Recognition
    • 7. Uses • Law Enforcement Security • Casinos, Super Bowl, Olympics • Border controlTransportation • E-passports • FacebookEntertainment • SceneTap
    • 8. Security Closed-circuit television (CCTV)  Surveillance technology crosschecked with mugshot databases
    • 9. Security Casinos Super Bowl  Tampa, Fl. (2001): 19 people identified London 2012 Olympics MORIS
    • 10. Transportation Germany: Fully automated border controls Australia: SmartGate  Compares the face of the individual with image in the e-passport microchip
    • 11. Entertainment  Facebook Tag Suggest  SceneTap  50 Chicago bars  Apps in progress  Apple
    • 12. New Developments ATM’s Advertising & marketing “Adidas is working with Intel to install and test digital walls with facial recognition in a handful of stores either in the U.S. or Britain. If a woman in her 50s walks by and stops, 60% of the shoes displayed will be for females in her age bracket, while the other 40% will be a random sprinkling of other goods. ‘If a retailer can offer the right products quickly, people are more likely to buy something,’ said Chris Aubrey, vice president of global retail marketing for Adidas.”
    • 13. “Facial Recognition TechnologyChallenges Privacy” http://abclocal.go.com/kgo/video?id=842585 5&syndicate=syndicate&section
    • 14. Limitations Not 100% accurate. Accuracy can fluctuate because of:  picture quality  Lighting  camera positions  facial expressions  and more
    • 15. Security Issues Mistaken identity cases Images cannot be used to convict suspects The CCTV cameras in London  1 crime solved per 1000 cameras
    • 16. CCTV Clip http://www.youtube.com/watch?v=fLEtzI1oe wI
    • 17. MORIS Mobile Offender Recognition and Information System Illegal search without a warrant No information is stored
    • 18. Facebook Has roughly 600 million users  that means that Facebook has a database of 600 million faces. Each time you “tag” a photo, Facebook learns more about your face.
    • 19. Google Picasa uses the same tagging techniques People fear a face recognition update to the app Google Goggles.  the app may even be able to identify peoples SSNs just from the photo.
    • 20. Adam Harvey•CV Dazzle•Foundways tocheat facerecognition
    • 21. WHAT DO YOU THINK?

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