FACIAL RECOGNITION. Evolving Detection to Support Voluntary Self-Exclusion. Canadian Gaming Summit Vancouver, April 2011

Transcription

1 FACIAL RECOGNITION Evolving Detection to Support Voluntary Self-Exclusion Canadian Gaming Summit Vancouver, April 2011

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 2

3 Defining Program Requirements RG Overview Page 3

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 racing RG Overview Page 4

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 School Division on Addictions 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. RG Overview Page 5

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-Excluders RG Overview Page 6

7 Defining Program Requirements Context for Facial Recognition Objectives: To support players, evolve practices, build corporate reputation OLG decisions must consider: Vulnerable Player Segment Most Self-Excluders have significant problems Program Standards International dialogue on best practices Brand Integrity OLG is highly scrutinized RG Overview Page 7

8 Defining Program Requirements Program Priorities Decision to implement FR required the following criteria: SYSTEM PERFORMANCE Sufficient true hit rate Acceptable false positive rate Defensible cost RESPECT for PRIVACY Privacy by design approach Protection of images/data to exceed industry standards Images of non-self-excluders had to be deleted EASE of OPERATION RG Overview Page 8 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

9 Defining Program Requirements Partners in Facial Recognition Information Privacy Commissioner AGCO Regulator University of Toronto iview Systems RG Overview Page 9

10 Facial Recognition & Biometric Encryption RG Overview Page 10

11 Self Exclusion Technology Timeline Overall Approach Online and centralized SE system (no FR) FR+BE proven viable Privacy requirements finalized FR+BE tuned FR+BE solution confirmed by IPC Build production FR+BE system Rollout FR+BE technology at OLG»live April 2009»minor reduction in CIR»~50% reduction in false alarms»meetings with IPC staff»80% to 90% CIR for OLG volunteer group»detecting 30 times more SE than the current process»proposal vetted by iview, UofT, IPC and OLG Exec»rollout results are consistent with POC tests 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 Privacy by Design Privacy by Design: Privacy + Security We are discarding all captured images except correctly recognized alerts HASH FR PI TMPL NAME IMAGE ADDR BE RG Overview Page 12

13 Face Recognition Performance Control Group Results Correct Identification Rate Entrance Additional Camera 30% 49% 80% 88% Lighting Improvements Improvements Lighting Positioning 91%»baseline at Casino SSM Apr. 2009»test at Foster Drive Oct. 2009»test at Woodbine Slots Oct. 2009»test at Woodbine Slots Oct. 2009»test at Woodbine Slots Mar Note: All tests were controlled by using volunteer OLG employees to determine the Correct Identification Rate RG Overview Page 13

14 Human Side of Detection RG Overview Page 14

15 Human Side Role of Security Officers Must capture image correctly Carry out registration accurately DATA INPUT Potential hits appear on terminal Review and decide REVIEW HITS Confirm identity on gaming floor Complete the breach Appropriate reporting INTERCEPT RG Overview Page 15

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 16

17 RG Overview Page 17 Future Direction

18 Ensuring Performance Mystery Shop program with credible independent 3 rd party Technology and pattern reviews to augment the technology base Product upgrades to implement industry FR enhancements RG Overview Page 18

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 19

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 20

21 Additional Sources OLG/IPC paper: Privacy-Protective Facial Recognition: Biometric Encryption Proof of Concept 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), (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), (2010) RG Overview Page 21