Emerging Technologies-Is Retail Ready for Facial Recognition

6/5/2014

Speakers Emerging Technologies Is Retail Ready for Facial Recognition?

• Patrick McEvoy – Sr. Mgr. of Asset Protection Systems and Technology, Hudson Bay Company

• Matt Sack – President and CEO of Rume/Best Security Industries.

• Chris Helin – Director of Integrated Solutions, Rume/Best Security Industries.

Minority Report

What is it? • Facial Recognition is a biometric software application where measurements of an individuals face in a digital image are taken and compared to a database to determine or confirm that individuals identity. • Facial recognition can be done through both live video as well as through digital images.

How Does It Work? •

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System Architecture

Images are taken of subjects and loaded into the software's database. – Images generally must have - pixels in the face area to insure they are accurate. – Images in the database are given a profile to provide users a SOP if identified. Cameras located at choke points provide video to the system. An algorithm is used to identify faces from the video images. – This is referred to as a face capture. Software matches captured faces to the database. – Software scores the capture to possible matches in the database based on its similarity to the database images. Probable matches are then identified based on their high score. Users can determine thresholds to narrow or broaden the display of database matches.

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Traditional Uses LIVE DEMO

• Government as both a live and forensic tool • Airports, Train Stations and large arenas for public safety • Offices for access control. • Gaming for identifying cheats and individuals with gambling addictions.

History In Retail • Facial Recognition is not new to retail. • Many retailers have dabbled in its use for years. • Lack of adoption.

IS RETAIL READY NOW?

– It just didn't work – It was too costly – Didn’t see the benefit – Just were not ready

Uses In Retail • Three Major Categories of Use in Retail – Uses for detecting and identifying known bad actors. • • • •

Recidivism BOLO Identification Dishonest or termed Employees Terrorism

SO YOU THINK YOU’RE READY?

– Uses that can drive customer satisfaction and sales. • VIP Customer Identification • Targeted Marketing • Customer demographics

– General everyday physical security. • Access Control • Capacity Management

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Technical Requirements •

Cameras – Several companies require specific cameras running uncompressed video. – Mounting of cameras requires angles of around 15 to 30 degree for consistent capture. – Cameras are focused to capture faces within a small area of roughly 4 cubic feet. •

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Multiple cameras are often needed to effectively capture at a double door entrance.

Server Intensive – Multiple servers needed to run face capture and database matching. • • •

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At least 1 server is designed to run the matching software Several companies limit the cameras to 3 per server for processing of video captures. Average installation could require rack of servers for an install of 15 to 20 cameras.

Database Photos – Photos must be of a certain resolution for optimal use in matching to live video. • •

Processing photos require good lighting and good positioning of the subject. Challenges can occur in using past video for purpose of matching due to quality.

Prior to Installation • Identify the scope of use of the technology. – Limit use to individuals deemed to be bad actors.

• Identify choke points where capture/identification would be optimal. • Review with Legal – Do not use technology to track customer activity – Do not use technology to track ethnicity or race. – Do not act on identification alone.

Use Case: Saks Fifth Avenue • Primary reason for use of Facial Recognition was for the identification of bad actors. – Desire to identify ORC groups and professional thieves. – Wanted real time alerts to provide actionable intelligence. – Use as a deterrent of future thefts – Use as a layer of protection from Terrorist acts.

Implementation • 12 Cameras to cover 6 customer entrances plus the employee door. • 1 Server per camera to process video and capture faces. • 2 Servers to house database and perform matching.

Findings FACIAL RECOGNITION IN ACTION AT SAKS MOVIE CLIP

• Technology requires high level of specificity to accomplish consistent results. • Desired results are definitely obtainable. • Real Time Alerting provides actionable Intelligence. • Thresholds of matching will vary depending on use. – If monitored, threshold is lower and verification is determined live by operator. – Automatic alerting on higher thresholds.

• Database Image requirements can be restrictive. • Camera and Server requirements can be cumbersome.

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Cost • Understanding pricing structure. – Purchase software licenses for cameras/servers. – Purchase site license based on location.

• Significant cost in hardware including servers and cameras. • Labor and technical costs can vary based on size of install and complexity. • Establishing an ROI on the technology.

Adoption Issues • Privacy Concerns – Is the Retail Customer ready? – Recent Backlash from customers on targeted marketing.

• Legal Concerns – Customers file suit alleging violation of privacy. – Lawsuits stemming from action taken based on the alerts.

• Do you advertise the use or not?

What’s Next • 3D Modeling for more accurate Facial Recognition. • Facial Recognition within Google Glasses • Analytic moves to the edge.

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