leg2a facial-recognition cga-april-2011-final
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RECOGNITION FACIAL
Evolving Detection to Support Voluntary Self-Exclusion
Canadian Gaming Summit Vancouver, April 2011
RG Overview Page 2
Contents
1. Defining the Program Requirements PAUL
2. Facial Recognition & Biometric Encryption KLAUS
3. The Human Side of Detection PAUL
4. Future Direction KLAUS
RG Overview Page 3
Defining Program Requirements
RG Overview Page 4
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”
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
Applies to slots/casino sites in Ontario Does NOT apply to lottery, bingo, horse racing
RG Overview Page 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
“… responsibility for self-exclusion and ultimately gambling remains with the
patron… Even the name, self-exclusion, 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.”
Dr. Howard J. Shaffer Harvard Medical
School – Division on Addictions
RG Overview Page 6
Defining Program Requirements Why Attempt to Detect Self-Excluders at All? 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-Excluders
NOT Deterrent to Breaching Policing
RG Overview Page 7
Defining Program Requirements Context for Facial Recognition
Objectives: To support players, evolve practices, build
corporate reputation
Program Standards
Vulnerable Player Segment
Brand Integrity
International dialogue on best practices
Most Self-Excluders have significant problems
OLG is highly scrutinized
OLG decisions must consider:
RG Overview Page 8
Defining Program Requirements
Program Priorities
• “Privacy by design” approach • Protection of images/data to exceed industry standards • Images of non-self-excluders had to be deleted
Decision to implement FR required the following criteria:
SYSTEM PERFORMANCE
RESPECT for PRIVACY
EASE of OPERATION
• Sufficient “true hit” rate • Acceptable “false positive” rate • Defensible cost
• Security officers use terminals at podium • System allows officers to review images that appear with a “hit”, in order to “make call” • Operate seamlessly with surveillance systems
RG Overview Page 9
Defining Program Requirements Partners in Facial Recognition
Information
Privacy Commissioner
AGCO
Regulator
University of
Toronto
iView
Systems
RG Overview Page 10
Facial Recognition & Biometric Encryption
RG Overview Page 11
Self Exclusion Technology Timeline
Online and centralized SE system
(no FR)
FR+BE proven viable
Privacy requirements
finalized
FR+BE solution
confirmed by IPC
Build production
FR+BE system
»minor reduction in CIR »~50% reduction in false alarms
Rollout FR+BE
technology at OLG
»live April 2009
»meetings with IPC staff
FR+BE tuned
»80% to 90% CIR for OLG volunteer group »detecting 30 times more SE than the current process
»rollout results are consistent with POC tests
»proposal vetted by iView, UofT, IPC and OLG Exec
Overall Approach
• 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 12
IMAGE
NAME
ADDR
PI
TMPL
FR
BE
HASH
Privacy by Design
We are discarding all captured images except
correctly recognized alerts
Privacy by Design: Privacy + Security
RG Overview Page 13
Face Recognition Performance
Lighting Improvements
»baseline at Casino SSM Apr. 2009
30% 49% Camera Positioning 88%
»test at Foster Drive Oct. 2009
»test at Woodbine Slots Oct. 2009
»test at Woodbine Slots Mar. 2010
91% Entrance Improvements
Additional Lighting 80%
»test at Woodbine Slots Oct. 2009
Note: All tests were controlled by using volunteer OLG employees to determine the Correct Identification Rate
Correct Identification Rate
Control Group Results
RG Overview Page 14
Human Side of Detection
RG Overview Page 15
Human Side
Role of Security Officers Must capture image correctly Carry out registration accurately
Potential “hits” appear on terminal Review and decide
Confirm identity on gaming floor Complete the breach Appropriate reporting
DATA INPUT
REVIEW “HITS”
INTERCEPT
RG Overview Page 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 Care
RG Overview Page 17
Future Direction
RG Overview Page 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 enhancements
RG Overview Page 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 platforms
RG Overview Page 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 research
RG Overview Page 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)
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