Leg2a facial-recognition cga-april-2011-final

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Leg2a facial-recognition cga-april-2011-final

  1. 1. FACIAL RECOGNITIONEvolving Detection to Support Voluntary Self-Exclusion Canadian Gaming Summit Vancouver, April 2011
  2. 2. Contents 1. Defining the Program Requirements PAUL 2. Facial Recognition & Biometric Encryption KLAUS 3. The Human Side of Detection PAUL 4. Future Direction KLAUSRG Overview Page 2
  3. 3. Defining Program RequirementsRG Overview Page 3
  4. 4. Defining Program Requirements Self-Exclusion Option to take a break from slot/casino gambling Self-help tool for players who are working to control behaviour OLG Role:  Provide clear information, implications for entering  Effectively deliver systems, policies, procedures  Stop direct marketing  Provide referrals as a “gateway” to a system of community service that are “individually tailored” Applies to slots/casino sites in Ontario Does NOT apply to lottery, bingo, horse racingRG Overview Page 4
  5. 5. Defining Program Requirements What Self-Exclusion is Not  Determination/judgement about a gambling problem  A policing program  A way to prevent people from gambling Dr. Howard J. Shaffer Harvard Medical “… responsibility for self-exclusion and School – ultimately gambling remains with the Division on patron… Even the name, self-exclusion, Addictions should serve to remind patrons, policy makers and industry observers that the responsibility for the behaviour of the gamblers who enroll in self-exclusion programs remains with them.”RG Overview Page 5
  6. 6. Defining Program Requirements Why Attempt to Detect Self-Excluders at All?Deterrent to Breaching NOT Policing  If a self-excluded person is detected, s/he will be escorted from site, and can be trespassed  Support by operator includes creating disincentives to breaching FR is not the answer on its own… it is on part of an overall perimeter of support for Self-ExcludersRG Overview Page 6
  7. 7. Defining Program Requirements Context for Facial RecognitionObjectives: To support players, evolve practices, build corporate reputationOLG decisions must consider:Vulnerable Player Most Self-Excluders have significant problems SegmentProgram Standards International dialogue on best practices Brand Integrity OLG is highly scrutinizedRG Overview Page 7
  8. 8. Defining Program Requirements Program PrioritiesDecision to implement FR required the following criteria: • Sufficient “true hit” rate SYSTEM • Acceptable “false positive” rate PERFORMANCE • Defensible cost • “Privacy by design” approach RESPECT for • Protection of images/data to exceed industry standards PRIVACY • Images of non-self-excluders had to be deleted • Security officers use terminals at podium EASE of • System allows officers to review images that OPERATION appear with a “hit”, in order to “make call” • Operate seamlessly with surveillance systemsRG Overview Page 8
  9. 9. Defining Program Requirements Partners in Facial Recognition Information AGCO Privacy RegulatorCommissionerUniversity of iView Toronto SystemsRG Overview Page 9
  10. 10. Facial Recognition & Biometric EncryptionRG Overview Page 10
  11. 11. Self Exclusion Technology Timeline Overall ApproachOnline and Privacy FR+BE Build Rollout FR+BEcentralized requirements FR+BE solution production FR+BE provenSE system finalized tuned confirmed FR+BE technology viable (no FR) by IPC system at OLG»live April »minor reduction »meetings with »80% to 90% CIR »proposal »rollout results are2009 in CIR IPC staff for OLG volunteer vetted by iView, consistent with POC tests »~50% reduction group UofT, IPC and in false alarms »detecting 30 times OLG Exec more SE than the current process • Measured approach to developing the system • Privacy by Design • Used staff control groups to measure system performance • Lighting and pose are key to facial recognition success • Field trial at Woodbine to validate system performance • Rollout to all sites RG Overview Page 11
  12. 12. Privacy by by Design: Privacy Design Privacy + Security We are discarding all captured images exceptcorrectly recognized alerts HASH FR PI IMAGE NAME TMPL ADDR BE RG Overview Page 12
  13. 13. Face Recognition Performance Control Group Results Correct Identification Rate 30% Lighting Improvements 49% Improvements 80% Entrance Additional Lighting 88% Camera Positioning 91%»baseline at »test at »test at »test at »test atCasino SSM Foster Drive Woodbine Slots Woodbine Slots Woodbine SlotsApr. 2009 Oct. 2009 Oct. 2009 Oct. 2009 Mar. 2010 Note: All tests were controlled by using volunteer OLG employees to determine the Correct Identification Rate RG Overview Page 13
  14. 14. Human Side of DetectionRG Overview Page 14
  15. 15. Human SideRole of Security Officers  Must capture image correctly DATA  Carry out registration accurately INPUT  Potential “hits” appear on terminal REVIEW  Review and decide “HITS”  Confirm identity on gaming floor  Complete the breach INTERCEPT  Appropriate reportingRG Overview Page 15
  16. 16. Human Side Duty of Care Implications?  Detecting SE who breach is a requirement of SE program–a support to discourage return to gaming sites  Photos in binders is one way to do this, FR is another  Duty of Care/Standard of CareRG Overview Page 16
  17. 17. Future DirectionRG Overview Page 17
  18. 18. Ensuring Performance  Mystery Shop program with credible independent 3rd party  Technology and pattern reviews to augment the technology base  Product upgrades to implement industry FR enhancementsRG Overview Page 18
  19. 19. Rollout and other Options  Approximately 20 sites remaining  Scheduled for completion Q2 of this fiscal  Off-site registration Process  Facial recognition can be extended to other areas of the casino – for example kiosks, non entrance locations, etc  Extend the facial recognition technology to other populations  Optimize the application for mobile platformsRG Overview Page 19
  20. 20. FR/BE Health Check and Enhancements  Post rollout review and tuning is an ongoing task  Privacy audit to validate the system design and implementation  Site adjustments – optimized and/or additional cameras  As detection levels fluctuate, understand why – SE program success versus system performance problems  Analysis, analytics and trending for RG and addiction researchRG Overview Page 20
  21. 21. Additional Sources  OLG/IPC paper:  Privacy-Protective Facial Recognition: Biometric Encryption Proof of Concept  http://www.ipc.on.ca/images/Resources/pbd-olg-facial-recog.pdf  IEEE pub:  Martin, K., Lu, H., Bui, F., Plataniotis, K. N. and Hatzinakos, D.: A biometric encryption system for the self-exclusion scenario of face recognition. IEEE Systems Journal: Special Issue on Biometrics Systems 3(4), 440-450 (2009)  IEEE pub:  Lu, H., Martin, K., Bui, F., Plataniotis, K. N. and Hatzinakos, D.: Face recognition with biometric encryption for privacy-enhancing self-exclusion. (2009)  IEEE pub:  Bui, F.M., Martin, K., Lu, H., Plataniotis, K.N., and Hatzinakos, D.: Fuzzy Key Binding Strategies Based on Quantization Index Modulation (QIM) for biometric Encryption (BE) Applications. IEEE Transactions On Information Forensics and Security 5(1), 118-132 (2010)RG Overview Page 21

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