Redefine Precision Policing with Lumen

Advancing law enforcement technology, Lumen offers a robust information engine to help identify the “who,” not just the “where” and “when.” In their latest video, Numerica showcases how Lumen works to challenge tedious data mining and put unprecedented levels of information into the hands of those who need it most.

“In policing, every second counts and this video helps show potential users how advanced, yet intuitive, Lumen is for all levels of law enforcement,” said Brian Strock, manager of Numerica Corporation’s public safety group. “Our solution helps narrow from millions of possible offenders to a handful of likely suspects in seconds using simple, but powerful investigative tools.”

With Lumen, users can search internal and cross-jurisdictional data sources to see a simple “baseball card view” of subjects, including information such as past crime history, case reports, known affiliations, addresses, phone numbers, vehicles, and more. Connect your disparate data sources such as ALPR, CAD, public records, intel, file archives, and virtually any other electronic records for a one-stop shop for law enforcement to search, analyze, and share.

Learn more about Lumen HERE.

The Rapidly Changing Landscape of Facial Recognition

See this article in Police Chief Magazine HERE.

Don Wick, Chief (Ret.), Arvada, Colorado, Police Department

Facial recognition is a technology that seems like it will soon be everywhere, from unlocking one’s phone to checking in for a flight at the airport. A fast food restaurant in China now allows diners to “Smile to Pay,” using facial recognition to identify the customer and deduct payment from his or her account automatically.1 Multiple airlines are currently testing facial recognition at some gates to eliminate the need for even an electronic boarding pass.2

As one might imagine, governments worldwide are also adopting facial recognition software. Police in the United Kingdom used a police van equipped with facial recognition technology to recognize and arrest a wanted man on the street.3 The Chinese government is even using it to identify frequent jaywalkers at major intersections.4

Law enforcement professionals in the United States have been using facial recognition for a number of years now. The FBI’s Next-Generation Identification Interstate Photo System (NGI-IPS), for example, first operated as a pilot program in 2011 before becoming fully operational in 2016. Part of a larger biometrics system the FBI runs that will cost over $1 billion to fully deploy, NGI-IPS contains more than 30 million photos that can be searched using facial recognition technology.5

Many state and local agencies have procured their own solutions as well, but deployment has typically been limited to only the largest agencies due to budgetary constraints. However, with the rise of new facial recognition technology that is both more affordable and better than what came before, law enforcement agencies that previously could not afford it might now be able to acquire their own facial recognition solutions. They might also be able to deploy the technology in ways that were previously impossible, such as on a mobile phone, body camera, or dash cam.

Facial Recognition FAQs

Before acquiring facial recognition technology, agencies should understand what the technology is capable of and how it can be used, including the answers to these common queries.

What is facial recognition?

Facial recognition uses image processing and machine learning algorithms to match a photo of an unidentified person (a “probe” photo) against a database of photos of identified persons. Most face identification algorithms will typically produce a list of possible matches, with each match having a score that indicates the quality or likelihood of a match.

Low resolution, poor lighting, motion blur, glare, off-angle faces (tilted, turned to the side, looking up or down), facial hair, glasses, hats, and other details of the probe photos can challenge algorithms to produce a good match. Advances in technology based on algorithms such as “deep learning,” however, have produced significant gains when processing challenging probe photos. The best systems will likely surpass human capabilities for facial recognition in the near future.

What is face detection?

In face detection, the algorithm attempts to detect faces in an image, without necessarily identifying whose face it is. It may also locate features, such as eyes, nose, mouth, and ears; detect the presence of beards, mustaches, eyeglasses, and hats; and identify gender, race, approximate age, and emotional state. This can be used for many different purposes, including gathering statistics on a large group of people (such as visitors to a building), measuring the reaction of people to a new product, or even determining the possible intent of individuals in a crowd. Many facial recognition algorithms are capable of face detection as well.

What are the law enforcement uses of facial recognition and face detection technology, and how does the public perceive them?

Public perception is an important aspect to consider whenever new technology becomes available to law enforcement. Even though the technology may be perfectly legal when used in appropriate circumstances, lack of information or even misinformation can cause a negative reaction on the part of the public. As a result, it is important for law enforcement decision makers to fully understand the spectrum of possible uses of the technology, as well as how the public may perceive those uses.

The simplest and most common use of facial recognition software is to search a database of known offenders for matches of an unidentified suspect in a criminal incident. A prime example of this is a security camera video image of a suspect shoplifting at a retail store. Detectives confront similar scenarios on a regular basis and often have little evidence to go on other than the video and perhaps an eyewitness who might not remember much. However, it is unlikely that even the best facial recognition system would generate just a single match from a security camera photo. Instead, the system will generate a list of possible matches, and the detectives working the case will need to use standard investigative methods to either rule out or further investigate each match, as they would with any investigative lead.

Facial recognition holds the promise to generate leads on a great many such cases that might otherwise go unsolved. Each case might not be especially high profile, but, in aggregate, these cases represent a staggering amount of criminal activity. Given that, even a modest increase in closure rates would be significant. Shoplifting, for example, generates billions of dollars in losses every year in the United States, where there are estimated to be almost a million “professional” shoplifters operating, including international shoplifting rings. Stopping even a single shoplifter could prevent tens of thousands of dollars in future theft.

There are numerous other examples of rarer but higher profile crimes, ranging from terrorism to mass shootings to kidnapping cases, in which the only initial clue to the suspect’s identity was a security camera photo. Although the cases are different, the use of facial recognition in these cases is essentially the same: identify an unknown suspect by searching a photo against a database of known offenders.

As facial recognition technology advances, however, other uses may become more widespread. Potentially, these could include the following capabilities:

• Match an unidentified suspect photo obtained in association with a criminal incident with a state database of driver’s license photos.
• Match a photo taken with permission (of a suspect or field contact, for example) using an officer’s smartphone with a database of driver’s license or jail booking photos.
• Search in real time to match people entering a courthouse with a database of wanted person photos.
• Perform a real-time search of airport travelers to match with a database of known terrorists.
• Search in real-time from a vehicle-mounted camera to match passersby with a database of wanted person photos.
• Search in real-time from a vehicle-mounted camera to match and record the likely identity, time, and location of passersby using a database of driver’s licenses and state identification photos.

Many of these capabilities are already a reality today. The key differences between all of these uses come down to two questions: (1) Where and how did law enforcement obtain the probe photo? (2) Where and how did law enforcement obtain the database of photos?

The easiest scenario to explain to the public is when both the probe photo and the database are obtained in direct association with criminal activities. If the probe photo is a security camera image and the database is a set of jail booking photos, for example, even the most ardent privacy rights advocate would probably find this use acceptable. If the probe photo is of a person with no known or suspected criminal activity and the database is also a non-criminal database, however, one can imagine the potential for public outcry.

By comparison, consider some possible uses of facial recognition in commercial environments, which are already a reality today:

• Pay at a fast-food restaurant.
• Automate driver check-in for ride-sharing services.
• Check in for a flight without a boarding pass.
• Automatically identify known customers in a retail store, storing their browsing habits, attention, and estimated emotional state for later analysis.
• Automatically identify known shoplifters or disgruntled former employees in a retail store and alert store security.

While commercial firms do not have the law enforcement powers of government, they also do not operate under the same legal framework or strictures as a government. As a result, they can often engage in practices that would be inadvisable or even illegal for a government entity. Several of the examples above illustrate this point clearly. When considering facial recognition technology for a law enforcement agency—and when answering questions from the public on how an agency uses such technology—it can be useful to understand the extent to which commercial firms are racing far ahead of many governments.

Can facial recognition be used in the cloud?

Finally, as more agencies move to using cloud solutions to reduce costs and improve reliability, it is natural to ask if facial recognition can be done in the cloud. Facial recognition used to require an on-premise deployment on an agency’s own servers, but that is no longer the case. More and more providers of facial recognition software offer their solutions in a cloud deployment, which means that there is no software to install and no servers to manage. The key questions to address are how secure the cloud provider is and what the provider’s stance is with respect to CJIS compliance (in the United States) or applicable regulations in the agency’s country. There are multiple cloud providers today offering facial recognition in a CJIS-compliant cloud environment.

Clearly, facial recognition software has come a long way and can play a critically important role in law enforcement in the future. Therefore, it is essential for law enforcement agencies to take proper precautions, both in purchasing and using this technology. Preparing the public for how facial recognition software works, what it can (and can’t) do, and how it can have a positive impact on reducing crime will go a long way toward creating an atmosphere of cooperation and trust.

Don Wick recently retired as chief of the Arvada, Colorado, Police Department. He currently serves as director of operations at Numerica Corporation, where he focuses on Lumen, Numerica’s law enforcement search, analysis, and data sharing platform.

1 “Just Smile: In KFC China Store, Diners Have a New Way to Pay,” Reuters, September 1, 2017.
2See, for example, Laura EntisJetBlue and Delta Are Testing Facial Recognition and Fingerprints to Replace Boarding Passes,” Fortune, June 1, 2017; Sean O’Kane, “British Airways Brings Its Biometric Identification Gates to Three More US Airports,” The Verge, March 9, 2018.
3Nick Summers, “UK Police Make First Arrest Triggered by Facial Recognition,” Engadget, June 6, 2017.
Christina Zhao, “Jaywalking in China: Facial Recognition Surveillance Will Soon Fine Citizens via Text Message,” Newsweek, March 27, 2018.
FBI, Criminal Justice Information Services, “Next Generation Identification (NGI).”
6 Read Hayes, Organized Retail Crime Annual Report, 2008.

Case Study: How the Mesa County Sheriff’s Office Used Lumen to Implement an Intelligence-Led Approach to Policing to Reduce Preventable Crime by 31% in High-Crime Areas.



The Mesa County, Colorado Sheriff’s Office was experiencing a marked increase in crime, particularly in the areas of property crime, homicide, and sexual assault. The office also recognized that there was a significant gap between the data contained in its information management system and the information that was readily available to its deputies on the street.


  • Increase the use of crime-related data
  • Change the way the office was visualizing criminal activity
  • Track criminals (and the crimes they commit) as they travel from jurisdiction to jurisdiction


  • Used modern web technology to bring all of the office’s data sources into a single, integrated system
  • Offered Lumen mobile and desktop applications to make crime-related data searchable, readily accessible, and easy to use
  • Changed the way the office was looking at criminal activity by enabling it to track and overlay data, identify trends and “hot spots” for criminal activity, and track criminal movement


  • Drove the office’s response to crime by allowing it to shift to a more intelligence-led approach to policing, which allowed patrols to connect data to crimes and gather information in real-time
  • Produced a 31% reduction in property crime in high-crime areas
  • Reduced calls for service, enabling the office to give more attention and staffing to geographic areas in which criminal activity was the highest

“Lumen has fundamentally changed our Sheriff’s Office and the way we look at crime.”
“Lumen has driven our response to crime, revolutionizing the way we handle day-to-day activities.”

-Sheriff Matt Lewis


The challenges of law enforcement data sharing

RAND has put out a new report, titled “Improving Information-Sharing Across Law Enforcement: Why Can’t We Know?”   Written by John Hollywood and Zev Winkelman, it makes some key points, including:

  • Law enforcement information sharing is a complex problem; there are many different systems to integrate.
  • Information assurance is a key issue.
  • Cost, especially for smaller agencies, is often a limiting factor.

Lumen addresses all of these.  Because Lumen does not have its own pre-defined data schema, data sources such as customized RMS and CAD systems can be integrated and shared quickly, easily, and at low cost.  And Lumen is CJIS compliant, ensuring the security of critical law enforcement data.  Contact us today to learn more!

CJIS Compliance

Ensuring compliance with the FBI CJIS Security Policy is an in-depth, comprehensive project that requires scrutinizing everything from software design to physical security and training.    While we had built our entire Lumen and CJISvault infrastructure from the ground up to be CJIS compliant (including putting all of our staff through the appropriate CJIS training and background checks), we wanted to be absolutely sure that we had done everything right.  That’s why we engaged with CJIS ACE at Diverse Computing to help us ensure that we exceed all of the requirements with flying colors.  To quote Bill Tatun, former New York State Police CJIS Systems Officer and CISO of Diverse Computing, “You are among the best prepared I’ve ever seen.”

Horizontal CJIS ACE seal

Body cameras: how expensive does storage have to be?

Body camera vendors are experiencing explosive growth in demand.  There’s no doubt that these small cameras can make a big difference in reducing issues related to interactions between police officers and the public.  But what is the true cost of body camera storage?  As noted recently on, some agencies are facing multimillion dollar annual bills just for storing and managing the videos.

That’s one of the reasons we introduced CJISvault.  We think that reliable, CJIS-compliant cloud storage of body camera videos, crime scene photos, and other electronic evidence doesn’t have to cost an arm and a leg.

Not only that, we also believe that critical data such as body camera videos shouldn’t be siloed inside yet another system.  Law enforcement agencies will get a lot more value out of their videos and photos if they can directly integrate them with RMS, CAD, and other data through Lumen, creating a user-friendly “one stop shop” for all of your data.  For less than the price of just storing your data with one of the big name body camera vendors, you can store your data securely in CJISvault and get the full-text search and powerful analytics of Lumen.  Interested?  Contact us for more details.




RAND Report on Information Technology Needs for Law Enforcement

RAND has put out a new report titled “High-priority Information Technology Needs for Law Enforcement.”    Thanks to RAND and John Hollywood for all of the hard work that clearly went into this report.  Top needs identified in the document include common operational picture to support day-to-day command and control,  knowledge management systems that can integrate multiple data streams such as RMS, CAD, and LPR cost effectively, and improved data interoperability and data sharing capabilities.

Lumen offers a unique and cost-effective solution to these needs.  If you were to talk to Lumen customers, you’d find that Lumen is meeting these three areas (and more!) day in and day out.   Contact us if you want to learn more!

Leaked document gives details on Palantir

TechCrunch has a story on Palantir today.   It sounds like a great product, but everything that they describe as a Palantir capability, Lumen can do too, and at a fraction of the price.   For example: “Detectives love the type of information it [Palantir] provides. They can now do things that we could not do before. They can now exactly see great information and the links between events and people.”  Or this one:  “Users do not have to use SQL queries or employ engineers to write strings in order to search…Instead, natural language is used to query data and results are returned in real-time.”  Again, check!

If this sounds like something you need, but you can’t afford Palantir’s seven-figure price tag, then Lumen is worth a look.



CJIS compliance and the Cloud

Chances are, you’ve heard of the cloud.  Every day, more businesses and more consumers are using software-as-a-service providers such as Amazon Web Services, Office 365, Dropbox, and others to provide on-demand software services at any scale.  The scalability and reliability are the major reasons why this move is happening.  Simply put, cloud vendors can realize economies of scale that an individual business or institution would be hard-pressed to match.   And they have the skills and knowledge necessary to implement best-in-class security measures.

Law enforcement poses a special challenge for cloud services.  Criminal history records, personally identifying information, and related information must be carefully controlled and used only for legally authorized purposes.  The FBI CJIS Security Policy lays out what we consider the ‘gold standard’ of how such information should be treated.  It includes the following measures:

  • Physical security.  Such information must be stored in a physically secure location to which only authorized personnel have access.
  • Logical security.  Authentication, encryption, and software access controls must be implemented according to standards set by the FBI.
  • Logging and auditing.  Security-related events must be securely logged and stored for auditing.

While the CJIS policy does allow for cloud computing, most vendors fail to meet the standards (for example, Google lost out on LA County’s business because Gmail isn’t CJIS compliant, while Microsoft Office 365 is).  When talking with a cloud vendor, be sure to ask about the CJIS policy.

If you are considering Lumen, you can rest assured that we have taken every measure to comply with the CJIS policy. Click to download our Lumen Security Brief, or contact us for more details.



What is enterprise search and why should you care?

Digital information is becoming increasingly important to law enforcement.  RMS, CAD, LPR, body camera video, cell phone forensics, mug shots,  digital crime scene photos, email boxes, and file archives are just a few of the data sources that must be managed, searched, and analyzed in a modern police department.  While there are many solutions that address a particular type of information, too often police personnel are left struggling to access 5 or 10 different internal systems just to piece together the information they need for one investigation or analyst report.

“Enterprise search” describes a suite of technologies designed to address this problem. As defined by Wikipedia, enterprise search is “the organized retrieval of structured and unstructured data within an organization.”  When searching for critical information, you want to be able to find and analyze it no matter where it lives in your agency.  And yet, most law enforcement software on the market today can only reach into one slice of data.

With Lumen, we’ve worked hard to implement an enterprise search and analytics platform specifically designed for the needs of law enforcement.  That means it is a secure, CJIS-compliant product inside and out.  It is fast; results appear in a fraction of a second.  And it is easy to get started, with minimal IT requirements and an intuitive interface.  If Lumen sounds like a solution that your agency needs, contact us today!