Facial Surveillance Deployment Complexity Examined

By: John Honovich, Published on Oct 11, 2009

Appreciating the key factors in deploying video analytics and facial surveillance are critical for setting the right expectations and achieving successful projects -- a key theme from our recent survey and recommendations on video analytic performance

Over the past few months, we have had an ongoing discussion about 3VR's facial recongition performance based on a field test of real-time facial alerts. In this dialogue, manufacturers, integrators and consultants asked various questions and exchanged ideas on deployment issues. Most notably, the manufacturer itself posted a variety of details. I recommend reading the thread.

Summary of Facial Recognition Deployment Complexity

The following list of items are important factors discussed in our thread about making facial recognition work for real time alerting:

  • Maximum width the camera can cover
  • Restrictions on where cameras can be placed
  • Impact of different levels of lighting
  • Ensuring faces are actually detected
  • Issues with obscured faces
  • Changes in appearance over time
  • Total cost of systems

3VR offers both facial alerting and searching.  Alerting is the use of a facial image of a person to generate a real-time notice when the person enters an area. Searching is the use of an image of a person's face to find when that person has previously been in an area (over days or weeks, etc.).

Searching is generally considered more tolerant to false matches because a searcher can quickly scan and discard the false matches. By contrast, for alerting, the monitor needs to individually review each alert whenever it arrives (usually immediately).

These considerations reviewed are extracted from the use of facial surveillance to generate real-time alerts.

Maximum width the camera can cover

While video surveillance users are accustomed to wide Field of Views (20 to 40 feet are common), the details needed for facial recognition require a much tighter field of view (FoV).

In the example cited in the Korean study, the FoV was approximately 12 feet wide (3.5 Meters) using a high definition 2MP camera. If a standard definition or analog camera was used, the maximum FoV would be half that (about 6 feet wide).

Get Notified of Video Surveillance Breaking News
Get Notified of Video Surveillance Breaking News

This, of course, limits what cameras can be reused given that most existing cameras are designed to capture much wider FoVs.

Also, given that most hallways and entrances with multiple doorways are greater than 6 feet wide, either multiple SD cameras or an HD camera would need to be used.

Restrictions on where cameras can be placed

The camera needs to be positioned fairly level to the faces being scanned (as shown in the sample photos).

Designers need to keep this in mind as surveillance cameras are commonly placed as significant downtilts (30 to 45 angles are common and produce top of head views). This positioning is often the easiest and least disruptive manner to deploy cameras.

Positioning cameras at angles optimized for facial recognition could incur additional cost, deployment complexity and potential aesthetic concerns.

Impact of different levels of lighting

As mentioned in the discussion, even lighting is important for real-time alerting. Even lighting produces the most consistent images improving for maximizing accuracy.

Many common issues in video surveillance can undermine even lighting. The most frequent challenge is sunlight either coming through windows or as external doors are opened. While external doorways are ideal for capturing faces as a person enters the facility (a natural chokepoint and early detection), many extneral doorways suffer from strong shadows due to sunlight surging into the building.

Ensuring faces are actually detected

3VR mentions that, "When cameras are properly and cleverly positioned, detection rates end up being ~80% for each single chokepoint camera."

To help rectify this, 3VR recommends "staging multiple choke points after another, and using 2 cameras per chokepoint."

This approach should definitely improve performance and bring detection close to 100% by increasing the chances for achieving a well lit shot with a clear view of the subject.

The most important consideration is the pure cost and complexity of deploying a series of cameras with optimized field of view, width, angle and lighting. Such a design strategy increases cost by thousands of dollars including the cost of cameras, installation and software licenses.

Accommodating Wider Areas / Entrances

Given the FoV and angle limitations, the width of areas to be covered are critical. A facility with narrow hallways and limited egresses is much simpler (and less expensive) to cover than a facility with large lobbies and numerous entrances.

As a practical example, a bank branch would be simpler to deploy than a big box retailer. Bank branches usually only have 1 or 2 entrances (though lighting could be an important issue) and a queue line. By contrast, big box retailers can have 20 - 30 foot wide entrances and very high ceilings. The combination of these two elements could require numerous cameras and special installation to obtain the minimal angles for facial surveillance.

Total cost of systems

For the Korean system, 3VR cited the cost for 4 facial recognition cameras and management as less than $50,000 USD: "1 x 3VR ServerClass IVMS and 4 3VR 2MP SmartCams, as well as the software upgrades needed, has a combined MSRP of less than $50,000"

3VR also offers an entry level appliance, the S-series, that supports up to 4 channels of facial recognition. The MSRP of the appliance with 2 channel licenses for facial recognition is $6,800 USD; cameras sold separately. Depending on traffic load and watchlist size, a more powerful appliance may be required. Also, if multiple choke points with 2 cameras per chokepoint design is used, this appliance would only cover one entry.

The cost of real-time facial alerting is certainly significantly higher than your standard IP video surveillance deployment. Now, if it catch thieves in the act, it can justify its cost. However, it does require a notable investment.

General Performance Constraints

These are important minimum elements for facial surveillance system and do not guarantee success. Performance may be impacted by attempts of individuals to avoid looking at cameras or obscure their faces. Furthermore, the quality of the facial recognition matching can vary by vendor.

Conclusion

These factors are important primary considerations in deploying facial recognition systems.

Related Reports

2020 Camera Book Released on Jan 10, 2020
This is the best, most comprehensive security camera training in the world, based on our unprecedented testing. Now, all IPVM PRO Members can get...
PlateSmart LPR Profile on Jan 31, 2019
PlateSmart Technologies claims to "turn any conventional surveillance camera into a license plate recognition camera" We spoke with PlateSmart to...
Milestone Machine Learning Camera Auto-Setting Examined on Mar 13, 2019
Milestone wants to improve image quality using Machine Learning to solve the problem of "a camera doesn't know what it is being used for",...
AI Video Tester Released on Apr 02, 2019
IPVM has released the world's first AI video tester that lets you see how various AI models (including from Amazon, Google, Microsoft and YOLO)...
Startup Spectral Edge RGB+IR Claims "Unique Low Light Performance Tech" on Apr 03, 2019
This UK startup, born out of university research, is boldly proclaiming that they deliver "Unique low light performance tech for video surveillance...
Covert Facial Recognition Using Axis and Amazon By NYTimes on May 20, 2019
What if you took a 33MP Axis camera covering one of the busiest parks in the US and ran Amazon Facial Recognition against it? That is what the...
Carnegie Mellon AI Startup Zensors Profile on Jun 11, 2019
Zensors is a startup formed by Carnegie Mellon graduates from a Carnegie Mellon research project, offering customized models per camera that they...
UK Facial Recognition Essex Errors Report on Jul 05, 2019
Facial recognition trials in the UK have generated significant controversy and debate over the past few years. This week, it flared again when Sky...
AI Video Surveillance (Finally) Goes Mainstream In 2020 on Sep 03, 2019
While video surveillance analytics has been promoted, hyped and lamented for nearly 20 years, next year, 2020, will be the year that it finally...
Gatekeeper Security Company Profile - Detecting Faces Inside Vehicles on Nov 14, 2019
Border security is a common discussion in mainstream US news and politics, as is the use of banned Chinese equipment by US Government agencies....

Most Recent Industry Reports

Every VMS Will Become a VSaaS on Feb 21, 2020
VMS is ending. Soon every VMS will be a VSaaS. Competitive dynamics will be redrawn. What does this mean? VMS Historically...
Video Surveillance 101 Course - Last Chance on Feb 20, 2020
This is the last chance to join IPVM's first Video Surveillance 101 course, designed to help those new to the industry to quickly understand the...
Vulnerability Directory For Access Credentials on Feb 20, 2020
Knowing which access credentials are insecure can be difficult to see, especially because most look and feel the same. Even insecure 125 kHz...
AI/Smart Camera Tutorial on Feb 20, 2020
Cameras with video analytics, sometimes called 'Smart' camera or 'AI' cameras, etc. are one of the most promising growth areas of video...
China Manufacturer Suffers Coronavirus Scare on Feb 20, 2020
Uniview suffered a significant health scare last week after one of its employees reported a fever and initially tested positive for coronavirus....
Cheap Camera Problems at Night on Feb 19, 2020
Cheap cameras generally have problems at night, despite the common perception that integrated IR makes cameras mostly the same, according to new...
Milestone Launches Multiple Cloud Solutions on Feb 18, 2020
Milestone is going to the cloud, becoming one of the last prominent VMSes to do so. Milestone is clearly late but how competitive do these new...
Video Surveillance Architecture 101 on Feb 18, 2020
Video surveillance can be designed and deployed in a number of ways. This 101 examines the most common options and architectures used in...
UK Stands Behind Hikvision But Controversy Continues on Feb 18, 2020
Hikvision is exhibiting at a UK government conference for law enforcement, provoking controversy from the press, politicians, and activists due to...
IronYun AI Analytics Tested on Feb 17, 2020
Taiwan startup IronYun has raised tens of millions for its "mission to be the leading Artificial Intelligence, big data video software as a service...