1. Christina Carr, Becky Schaffran, & Tess Cimini
FACIAL RECOGNITION

8. What is it?
 Facial recognition systems are built on
computer programs that analyze images of
human faces for the purpose of identifying
them.

9. 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

10. 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

11. 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

12. Steps of 3D Recognition

13. Uses
• Law Enforcement
Security
• Casinos, Super Bowl, Olympics
• Border control
Transportation
• E-passports
• Facebook
Entertainment
• SceneTap

14. Security
 Closed-circuit television (CCTV)
 Surveillance technology crosschecked with mugshot
databases

15. Security
 Casinos
 Super Bowl
 Tampa, Fl. (2001): 19 people identified
 London 2012 Olympics
 MORIS

16. Transportation
 Germany: Fully automated border controls
 Australia: SmartGate
 Compares the face of the individual with image in
the e-passport microchip

17. Entertainment
 Facebook Tag
Suggest
 SceneTap
 50 Chicago bars
 Apps in progress
 Apple

18. 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.”

19. “Facial Recognition Technology
Challenges Privacy”
 http://abclocal.go.com/kgo/video?id=842585
5&syndicate=syndicate&section

20. Limitations
 Not 100% accurate.
 Accuracy can fluctuate because of:
 picture quality
 Lighting
 camera positions
 facial expressions
 and more

21. Security Issues
 Mistaken identity cases
 Images cannot be used to convict suspects
 The CCTV cameras in London
 1 crime solved per 1000 cameras

22. CCTV Clip
 http://www.youtube.com/watch?v=fLEtzI1oe
wI

23. MORIS
 Mobile Offender Recognition and
Information System
 Illegal search without a warrant
 No information is stored

24. 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.

25. 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.

26. Adam Harvey
•CV Dazzle
•Found
ways to
cheat face
recognition

27. WHAT DO YOU THINK?