Facial Recognition Software for Law Enforcement: Responsible Investigations and Public Privacy
Table of Contents
- Introduction
- The Need for Facial Recognition in Modern Policing
- Key Features of Effective Facial Recognition Systems for Law Enforcement
- The Importance of Accuracy and Reducing Bias in FR Systems
- Facial Recognition vs Facial Identification
- Facial Recognition and Lakota Software
Facial recognition technology (FRT) is becoming a highly sought-after resource within the public safety industry.
By scanning and comparing facial features of faces extracted from photos or video clips, these systems identify individuals quickly and efficiently, which can help speed up investigations.
But, as FRT becomes more widely used, the more complicated its use becomes. The difficulty is proving that it’s being used responsibly to promote public safety without violating privacy rights. The FRT has to be accurate, used ethically, and within the guard rails of concise policies that protect people’s rights. The technology can’t be a silver bullet! If the adoption of FRT in law enforcement is paramount, then it’s going to be key for law enforcement to show the public how facial recognition technology is improving everyday policing.
The Need for Facial Recognition in Modern Policing
Law enforcement is up against a tangled web of new and changing threats, making things a lot tougher for traditional policing. Our officers need the latest and greatest tools to thwart these threats efficiently and safely.
That’s where facial recognition technology becomes an advantage because this technology can help solve crimes faster.
FRT can provide real-time matching of known suspects. Think about big events—concerts, sporting events, protests—where crowds are massive. If used appropriately, FRT can identify bad actors in real-time by comparing the spectators’ faces against a watchlist of known individuals or suspects. This provides the officers on the ground with the intelligence they need to keep people safe without throwing a wrench into the event itself. However, this use-case requires concise policies to ensure the investigators are not solely relying on the “match” created by the FRT. There are documented cases of false arrests that were made because the FRT results were not validated by the law enforcement agency.
Another key usecase for FR technology is finding missing people. The old ways—like passing out flyers or following up on tip lines—can be way too slow and potentially inaccurate. But when FRT is connected to public surveillance in places like malls, airports, and transit stations, a wider net is cast to help find those missing people.
The same goes for border control: FRT can quickly check IDs at points of entry, thus making sure those identities are verified. This technology is a tremendous tool for helping prevent illegal entry and human trafficking.
Another use-case is using facial recognition software to expedite the investigative process of extracting facial identities from multimedia files, such as surveillance footage captured from a security camera. The software output gives the officer a working list of people to investigate further. The FR software, in this case, replaces the old-school method of pausing videos frame by frame, or searching through digital images, to find facial identities to investigate. This obviously reduces the officers’ workload by automating that whole process.
Regardless of the method being used, the FRT quickly compares facial data across different sources and automatically produces results for the end-users to analyze. Using this technology can help stop crimes before they happen, or reduce investigative lead time. Responsible use of FRT puts law enforcement in a better position to handle today’s threats, keeping communities safer and making the most of their resources.
Key Features of Effective Facial Recognition Systems for Law Enforcement
Facial recognition systems need a few key features to ensure useful results for law enforcement.
The top priority: accuracy! Factors such as bad lighting, older camera technologies, obstructed views, and poor angles will reduce accuracy, so the facial recognition algorithms have to be appropriately tested against each of these factors. The National Institute of Standards and Technology (NIST) is a de facto organization that tests FRT for accuracy. However, many of these factors can be eliminated before an FR system is installed.
Speed is another important factor. The end-users need to know they can rely on their FRT to provide quick results. Whether it’s for a security system allowing access to a building or a surveillance camera monitoring a public space, the results need to be instantaneous.
Interoperability with other systems is also important. An FR system needs to integrate with the systems that law enforcement already uses to store their biometric data, criminal records, and other key information. When systems link up easily, it speeds up cross-checking and confirming identities, making the FR system a strong addition to the agencies’ overall tech stack. Plus, if it integrates well, there’s less need for training and the systems become operational faster.
Finally, adding more ways to identify people—like using fingerprints, iris scans, or even voice recognition—makes facial recognition systems more robust. If you’re trying to confirm the identity of someone at a border or in a high-security area, the system could perform a multi-modal biometric verification. The same case can be made for using these other biometric modalities to authenticate the users of the FR system, thus preventing misuse.
The Importance of Accuracy and Reducing Bias in FR Systems
Although facial recognition algorithms continue to improve and achieve accuracy results well above 99%, they are not a silver bullet for identifying criminals. Another issue preventing the FRT from being a perfect system is bias. Studies show these systems can struggle with accurately identifying people from certain ethnicities. This bias often comes from training data that doesn’t reflect the diversity of the real world. When these biases are baked into the technology, they can lead to higher misidentification rates for some groups, thus raising ethical and legal red flags. Law enforcement needs to know that using biased technology doesn’t just cause errors; it could actually support systemic discrimination, making existing inequalities even worse.
To impress on the point made earlier, there have been multiple false arrests and convictions made because agencies solely relied on the FRT to make the case. This kind of mistake can seriously erode public trust, waste resources, and harm innocent people.
It’s about making sure justice is served and civil rights are upheld. When these systems mess up, like misidentifying someone, it could lead to arresting or detaining the wrong person. Worse yet, these errors can snowball into civil rights issues—like unlawful detentions, false accusations, or racial profiling—which can really shake people’s faith in law enforcement and the justice system overall.
Fixing this isn’t simple. It takes a lot of ongoing work to test and improve these algorithms, which most FR algorithm developers are constantly focusing on. By continually training these algorithms with newer and more diverse data, their accuracy will increase and bias will be reduced. The goal is to continually make these systems more reliable in real-life situations and fair for everyone. However, it’s hard to say if they will ever be 100%, especially since the FRT systems also rely on other factors, including: lighting, camera angles, and camera resolution. In order of for the FR software to be as accurate as possible, the supporting hardware and infrastructure will need to be evaluated as well.
Being open about how these systems are tested and validated is crucial for maintaining public trust. To get this right, law enforcement and facial recognition vendors both need to focus on accuracy, eliminating bias, and keeping things transparent. Making the testing data public helps reassure people that the technology is being evaluated correctly and fairly. It also keeps everyone accountable, giving civil rights groups, outside experts, and the public a chance to weigh in and push for improvements. It’s also about constantly learning and adjusting to new information and technology to maintain trust and respect for everyone’s rights. Sticking to these values is key if we want to protect civil liberties while still using advanced tools to keep people safe.
Law enforcement agencies and their tech partners should be upfront about how they test these tools—like what data they use, how they test it, and what the results look like. Doing so means deploying facial recognition that’s not just more effective but also more fair.
Facial Recognition vs Facial Identification
Facial identification and facial recognition both use facial biometrics but serve different purposes.
Facial identification is pretty straightforward. It’s all about using a facial recognition algorithm to automatically extract facial identities from a data source. Once the system provides the end-user with the extracted facial identities, they can begin to investigate those leads and try to identify them. One method of identification would be using an FR system to match the face against, which could provide instant identification or provide a candidate list of anyone that closely matched (e.g., a lineup card).
Additionally, facial recognition systems can also operate in real-time, and provide an instantaneous “match” of a known identity. To do this, the FRT is solely relying on the system to constantly parse the video footage and use its algorithm to compare the extracted faces against a known database. The systems are essentially autonomous but are reliant on the accuracy of the software.
This isn’t about matching one face to one other face that’s already in a system. Think of it like unlocking your phone with your face or the cops checking if someone they nabbed matches a mugshot in their files. It’s all about being super precise — it needs to be spot-on for it to work.
Now, facial recognition is a whole different ball game. This one scans a face and checks it against a whole bunch of faces in a big ol’ database. This makes it great for stuff like surveillance or monitoring. You see it in action when social media platforms suggest who to tag in a photo, or when security cameras in public places scan faces to pick someone out in a crowd.
The facial recognition method is certainly a very effective tool, but as mentioned earlier, it’s not 100% accurate and can lead to false arrests. On the other hand, a facial identification application can be an alternative for agencies to responsibly utilize “facial recognition” to streamline investigations.
Facial Recognition and Lakota Software
Facial recognition technology holds significant potential for enhancing law enforcement capabilities by providing faster, more accurate means of identifying individuals and solving crimes. However, the power of FR technology also comes with the responsibility to use it ethically and in compliance with privacy and civil rights standards.
Responsible use requires adherence to regulations, transparency in deployment, and a commitment to reducing biases through continuous testing and improvement. As law enforcement agencies consider the integration of FR systems, they must weigh the benefits against the ethical implications to maintain public trust.
Those interested in adopting FR technology should prioritize solutions that align with these principles, ensuring they contribute to both public safety and the protection of individual freedoms.
Lakota Software specializes in developing and integrating biometric and facial recognition solutions tailored for your specific use-case. With years of experience in creating accurate, reliable, and ethically sound systems, Lakota offers cutting-edge tools that help agencies enhance their investigative capabilities while respecting privacy concerns.
Lakota’s solutions, built to meet the highest standards of accuracy and compliance, empower law enforcement to make informed, real-time decisions without compromising public trust.
Key Takeaways
- Facial recognition technology is crucial for modern law enforcement, enabling faster crime solving, real-time threat detection, and efficient missing person searches.
- Effective FR systems prioritize accuracy, speed, integration, and potentially multimodal biometrics.
- Accuracy and bias reduction are critical to avoid wrongful arrests and discrimination. Continuous testing and transparency are key.
- Ethical use involves balancing public safety with privacy and civil rights through adherence to regulations and community engagement.
- Facial identification matches a face to a single record, while facial recognition scans against a database, highlighting the need for ethical considerations.
- Lakota Software specializes in accurate, reliable, and ethically sound FR solutions for law enforcement.
Sam Cilento
Business Development Manager
Sam Cilento is an accomplished Industrial and Systems Engineer with a track record in biometrics, systems engineering, quality control, and supply chain management. His 15-year career is marked by a profound commitment to advancing biometric software solutions. As a Consulting Business Owner, Certified Project Management Professional (PMP), and Lean Six Sigma Green Belt, Sam has been integral in driving Lakota Software Solutions’ mission to provide innovative and affordable biometric identification systems.