RECOGNITION FACIAL

Evolving Detection to Support Voluntary Self-Exclusion

Canadian Gaming Summit Vancouver, April 2011

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Contents

1. Defining the Program Requirements PAUL

2. Facial Recognition & Biometric Encryption KLAUS

3. The Human Side of Detection PAUL

4. Future Direction KLAUS

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Defining Program Requirements

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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

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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

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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

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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:

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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

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Defining Program Requirements Partners in Facial Recognition

Information

Privacy Commissioner

AGCO

Regulator

University of

Toronto

iView

Systems

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Facial Recognition & Biometric Encryption

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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

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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

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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

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Human Side of Detection

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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

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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

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Future Direction

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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

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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

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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

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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)