Enough ink has been spilled on the topic to fill libraries, but this article is meant to distill some of the key arguments, viewpoints, and general information related to facial recognition systems and the impacts they can have on our privacy today.

What Are Facial Recognition Systems?

The actual technology behind FRS and who develops them can be complicated. It’s best to have a basic idea of how these systems work before diving into the ethical and privacy-related concerns related to using them.

How Do Facial Recognition Systems Work?

On a basic level, facial recognition systems operate on a three-step process. First, the hardware, such as a security camera or smartphone, records a photo or video of a person.

That photo or video is then fed into an AI program, which then maps and analyzes the geometry of a person’s face, such as the distance between eyes or the contours of the face. The AI also identifies specific facial landmarks, like forehead, eye sockets, eyes, or lips.

Finally, all these landmarks and measurements come together to create a digital signature which the AI compares against its database of digital signatures to see if there is a match or to verify someone’s identity. That digital signature is then stored on the database for future reference.

Read More At: The Pros and Cons of Enlisting AI for Cybersecurity

Use Cases of Facial Recognition Systems

A technology like facial recognition is broadly applicable to a number of different industries. Two of the most obvious are law enforcement and security. 

With facial recognition software, law enforcement agencies can track suspects and offenders unfortunate enough to be caught on camera, while security firms can utilize it as part of their access control measures, checking people’s faces as easily as they check people’s ID cards or badges.

Access control in general is the most common use case for facial recognition so far. It generally relies on a smaller database (i.e. the people allowed inside a specific building), meaning the AI is less likely to hit a false positive or a similar error. Plus, it’s such a broad use case that almost any industry imaginable could find a reason to implement the technology.

Facial recognition is also a hot topic in the education field, especially in the U.S. where vendors pitch facial recognition surveillance systems as a potential solution to the school shootings that plague the country more than any other. It has additional uses in virtual classroom platforms as a way to track student activity and other metrics.

In healthcare, facial recognition can theoretically be combined with emergent tech like emotion recognition for improved patient insights, such as being able to detect pain or monitor their health status. It can also be used during the check-in process as a no-contact alternative to traditional check-in procedures.

The world of banking saw an increase in facial recognition adoption during the COVID-19 pandemic, as financial institutions looked for new ways to safely verify customers’ identities.

Some workplaces already use facial recognition as part of their clock-in-clock-out procedures. It’s also seen as a way to monitor employee productivity and activity, preventing folks from “sleeping on the job,” as it were. 

Companies like HireVue were developing software using facial recognition that can determine the hireability of prospective employees. However, it discontinued the facial analysis portion of its software in 2021. In a statement, the firm cited public concerns over AI and a growing devaluation of visual components to the software’s effectiveness.

Retailers who sell age-restricted products, such as bars or grocery stores with liquor licenses, could use facial recognition to better prevent underaged customers from buying these products.

Who Develops Facial Recognition Systems?

The people developing FRS are many of the same usual suspects who push other areas of tech research forward. As always, academics are some of the primary contributors to facial recognition innovation. The field was started in academia in the 1950s by researchers like Woody Bledsoe.

In a modern day example, The Chinese University of Hong Kong created the GaussianFace algorithm in 2014, which its researchers reported had surpassed human levels of facial recognition. The algorithm scored 98.52% accuracy compared to the 97.53% accuracy of human performance.

In the corporate world, tech giants like Google, Facebook, Microsoft, IBM, and Amazon have been just some of the names leading the charge.

Google’s facial recognition is utilized in its Photos app, which infamously mislabeled a picture of software engineer Jacky Alciné and his friend, both of whom are black, as “gorillas” in 2015. To combat this, the company simply blocked “gorilla” and similar categories like “chimpanzee” and “monkey” on Photos.

Amazon was even selling its facial recognition system, Rekognition, to law enforcement agencies until 2020, when they banned the use of the software by police. The ban is still in effect as of this writing.

Facebook used facial recognition technology on its social media platform for much of the platform’s lifespan. However, the company shuttered the software in late 2021 as “part of a company-wide move to limit the use of facial recognition in [its] products.”

Additionally, there are firms who specialize in facial recognition software like Kairos, Clearview AI, and Face First who are contributing their knowledge and expertise to the field.

Read More At: The Value of Emotion Recognition Technology

Is This a Problem?

To answer the question of “should we be worried about facial recognition systems,” it will be best to look at some of the arguments that proponents and opponents of facial recognition commonly use.

Why Use Facial Recognition?

The most common argument in favor of facial recognition software is that it provides more security for everyone involved. In enterprise use cases, employers can better manage access control, while lowering the chance of employees becoming victims of identity theft.

Law enforcement officials say the use of FRS can aid their investigative abilities to make sure they catch perpetrators quickly and more accurately. It can also be used to track victims of human trafficking, as well as individuals who might not be able to communicate such as people with dementia. This, in theory, could reduce the number of police-caused deaths in cases involving these individuals.

Human trafficking and sex-related crimes are an oft-spoken refrain from proponents of this technology in law enforcement. Vermont, the state with the strictest bans on facial recognition, peeled back their ban slightly to allow for its use in investigating child sex crimes.

For banks, facial recognition could reduce the likelihood and frequency of fraud. With biometric data like what facial recognition requires, criminals can’t simply steal a password or a PIN and gain full access to your entire life savings. This would go a long way in stopping a crime for which the FTC received 2.8 million reports from consumers in 2021 alone.

Finally, some proponents say, the technology is so accurate now that the worries over false positives and negatives should barely be a concern. According to a 2022 report by the National Institute of Standards and Technology, top facial recognition algorithms can have a success rate of over 99%, depending on the circumstances.

With accuracy that good and use cases that strong, facial recognition might just be one of the fairest and most effective technologies we can use in education, business, and law enforcement, right? Not so fast, say the technology’s critics.

Why Ban Facial Recognition Technology?

First, the accuracy of these systems isn’t the primary concern for many critics of FRS. Whether the technology is accurate or not is inessential. 

While Academia is where much research on facial recognition is conducted, it is also where many of the concerns and criticisms are raised regarding the technology’s use in areas of life such as education or law enforcement. 

Northeastern University Professor of Law and Computer Science Woodrow Hartzog is one of the most outspoken critics of the technology. In a 2018 article Hartzog said, “The mere existence of facial recognition systems, which are often invisible, harms civil liberties, because people will act differently if they suspect they’re being surveilled.”

The concerns over privacy are numerous. As AI ethics researcher Rosalie A. Waelen put it in a 2022 piece for AI & Ethics, “[FRS] is expected to become omnipresent and able to infer a wide variety of information about a person.” The information it is meant to infer is not necessarily information an individual is willing to disclose.

Facial recognition technology has demonstrated difficulties identifying individuals of diverse races, ethnicities, genders, and age. This, when used by law enforcement, can potentially lead to false arrests, imprisonments, and other issues.

As a matter of fact, it already has. In Detroit, Robert Williams, a black man, was incorrectly identified by facial recognition software as a watch thief and falsely arrested in 2020. After being detained for 30 hours, he was released due to insufficient evidence after it became clear that the photographed suspect and Williams were not the same person.

This wasn’t the only time this happened in Detroit either. Michael Oliver was wrongly picked by facial recognition software as the one who threw a teacher’s cell phone and broke it.

A similar case happened to Nijeer Parks in late 2019 in New Jersey. Parks was detained for 10 days for allegedly shoplifting candy and trying to hit police with a car. Facial recognition falsely identified him as the perpetrator, despite Parks being 30 miles away from the incident at the time. 

There is also, in critics’ minds, an inherently dehumanizing element to facial recognition software and the way it analyzes the individual. Recall the aforementioned incident wherein Google Photos mislabeled Jacky Alciné and his friend as “gorillas.” It didn’t even recognize them as human. Given Google’s response to the situation was to remove “gorilla” and similar categories, it arguably still doesn’t.

Finally, there comes the issue of what would happen if the technology was 100% accurate. The dehumanizing element doesn’t just go away if Photos can suddenly determine that a person of color is, in fact, a person of color. 

The way these machines see us is fundamentally different from the way we see each other because the machines’ way of seeing goes only one way.  As Andrea Brighenti said, facial recognition software “leads to a qualitatively different way of seeing … .[the subject is] not even fully human. Inherent in the one way gaze is a kind of dehumanization of the observed.”

In order to get an AI to recognize human faces, you have to teach it what a human is, which can, in some cases, cause it to take certain human characteristics outside of its dataset and define them as decidedly “inhuman.”

That said, making facial recognition technology more accurate for detecting people of color only really serves to make law enforcement and business-related surveillance better. This means that, as researchers Nikki Stevens and Os Keyes noted in their 2021 paper for academic journal Cultural Studies, “efforts to increase representation are merely efforts to increase the ability of commercial entities to exploit, track and control people of colour.”

Final Thoughts

Ultimately, how much one worries about facial recognition technology comes down to a matter of trust. How much trust does a person place in the police or Amazon or any random individual who gets their hands on this software and the power it provides that they will only use it “for the right reasons”?

This technology provides institutions with power, and when thinking about giving power to an organization or an institution, one of the first things to consider is the potential for abuse of that power. For facial recognition, specifically for law enforcement, that potential is quite large.

In an interview for this article, Frederic Lederer, William & Mary Law School Chancellor Professor and Director of the Center for Legal & Court Technology, shared his perspective on the potential abuses facial recognition systems could facilitate in the U.S. legal system:

“Let’s imagine we run information through a facial recognition system, and it spits out 20 [possible suspects], and we had classified those possible individuals in probability terms. We know for a fact that the system is inaccurate and even under its best circumstances could still be dead wrong.

If what happens now is that the police use this as a mechanism for focusing on people and conducting proper investigation, I recognize the privacy objections, but it does seem to me to be a fairly reasonable use.

The problem is that police officers, law enforcement folks, are human beings. They are highly stressed and overworked human beings. And what little I know of reality in the field suggests that there is a large tendency to dump all but the one with the highest probability, and let’s go out and arrest him.”

Professor Lederer believes this is a dangerous idea, however:

“…since at minimum the way the system operates, it may be effectively impossible for the person to avoid what happens in the system until and unless… there is ultimately a conviction.”

Lederer explains that the Bill of Rights guarantees individuals a right to a “speedy trial.” However, court interpretations have borne out that arrested individuals will spend at least a year in prison before the courts even think about a speedy trial.

Add to that plea bargaining:

“…Now, and I don’t have the numbers, it is not uncommon for an individual in jail pending trial to be offered the following deal: ‘plead guilty, and we’ll see you’re sentenced to the time you’ve already been [in jail] in pre-trial, and you can walk home tomorrow.’ It takes an awful lot of guts for an individual to say ‘No, I’m innocent, and I’m going to stay here as long as is necessary.’

So if, in fact, we arrest the wrong person, unless there is painfully obvious evidence that the person is not the right person, we are quite likely to have individuals who are going to serve long periods of time pending trial, and a fair number of them may well plead guilty just to get out of the process.

So when you start thinking about facial recognition error, you can’t look at it in isolation. You have to ask: ‘How will real people deal with this information and to what extent does this correlate with everything else that happens?’ And at that point, there’s some really good concerns.”

As Lederer pointed out, these abuses already happen in the system, but facial recognition systems could exacerbate these abuses and even increase them. They can perpetuate pre-existing biases and systemic failings, and even if their potential benefits are enticing, the potential harm is too present and real to ignore.

Of the viable use cases of facial recognition that have been explored, the closest thing to a “safe” use case is ID verification. However, there are plenty of equally effective ID verification methods, some of which use biometrics like fingerprints.

In reality, there might not be any “safe” use case for facial recognition technology. Any advancements in the field will inevitably aid surveillance and control functions that have been core to the technology from its very beginning.

For now, Lederer said he hasn’t come to any firm conclusions as to whether the technology should be banned. But he and privacy advocates like Hartzog will continue to watch how it’s used.

Read Next: What’s Next for Ethical AI?

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However, face masks also present a problem to facial recognition software, blocking several facial characteristics that the software would otherwise use to make an ID. Clearview AI, a company that creates facial recognition software aimed at law enforcement agencies and boasts a photo database of over 10 billion images, says it has solved this problem. The company pulls photos from news media, mugshot websites, and social media profiles.

In reality, mask removal and enhancement features on facial recognition software cross a line, and businesses should think twice before using them. Because so many of Clearview AI’s customers are law enforcement agencies, it’s likely that these new features will be used to make arrests.

Facial recognition’s mask removal

• What does mask removal do?
• What’s the problem with using facial recognition AI to “remove” masks?
• The dangers of increasing facial recognition use
• Businesses must be cautious about using this technology

What Does Mask Removal Do?

Facial recognition uses artificial intelligence (AI) to analyze the geometry of a person’s face, including features like the distance between their eyes and the shape of their chin. The new mask removal tools would basically use other photos in the AI’s database to guess at what a person might look like under their mask. The model takes data points from the part of the face it can see (the eyes, forehead, and possibly the ears), and then attempts to match those to other images using statistical patterns to determine possible facial characteristics that the mask is hiding.

These features could be helpful for emotion recognition and advertising, as long as organizations use them with permission. For example, medical staff could determine a patient’s level of pain or discomfort in a waiting room without being able to see their whole face, allowing them to determine which patients they need to see first. However, many of Clearview’s clients seem to be law enforcement agencies.

What’s the Problem with Using Facial Recognition AI to “Remove” Masks?

Considering how much of a person’s face a mask actually blocks, that means about two-thirds of a facial recognition match using these features would be strictly guesswork. We already know that facial recognition has some major issues with accuracy, especially when it comes to identifying women of color, so adding guesswork on top of that is just asking for trouble. 

“I would expect accuracy to be quite bad, and even beyond accuracy, without careful control over the data set and training process, I would expect a plethora of unintended bias to creep in,” said MIT professor Aleksander Madry in an interview with Wired. Facial recognition models already don’t get enough training with people of color, so the likelihood of the model accurately identifying a non-white person with a mask on is extremely low.

Carlos Anchia, CEO of Plainsight, explains how this technology would work. “Attempting to apply the technology to facial feature prediction is fraught with complexity and potential for inaccuracy,” he says. “In one approach to automating a prediction of features hidden by masks, the model would first remove the mask in the image and then create a void. This void would need to have that portion of the face replaced with predicted facial features resulting from the matching images. In cases like this, confidence in the predictive (altered) image would likely be low and would require an enormous amount of data for each image/person.”

Also read: The Struggles & Solutions to Bias in AI

The Dangers of Increasing Facial Recognition Use

One of the issues with increasing facial recognition use is that many users, especially those in law enforcement, don’t really seem to be addressing how inaccurate the technology is. Also, as we learned from the recent Facebook hearings, AI algorithms require human oversight for the best results, but understaffed organizations may not provide this, especially if it won’t help their bottom line.

“My intention with this technology is always to have it under human control. When AI gets it wrong it is checked by a person,” Clearview AI co-founder and CEO Hoan Ton-That told Wired. As great as that sounds, we know that organizations don’t always use technology exactly the way it was originally intended. After all, facial recognition isn’t the only problematic “science” law enforcement agencies use to catch criminals, so there’s no guarantee that they won’t use this incorrectly as well. 

Businesses Must Be Cautious About Using this Technology

While there’s an obvious demand for accurate facial recognition technology, businesses have to be careful about using it, especially in its current iteration. Anchia says, “With the new Clearview AI technology, the only data points that are common from image-to-image of individuals would be the exposed (unmasked) images. To perform at operational accuracy with a high degree of robustness, machine models often require additional data points to bolster the confidence in the predictions. In these cases, the large number of data points required to achieve high-accuracy prediction quality is not present.”

Facial recognition AI is, unfortunately, not accurate enough to make life-changing decisions. Instead, businesses can use it to improve their product lines or give employees passwordless access to devices. Using facial recognition in these ways helps avoid some of the bias issues that the technology brings with it, while still giving it a chance to improve its accuracy.

Read next: Edge AI: The Future of Artificial Intelligence and Edge Computing

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AT&T says it may reach 14 million customers with fiber-to-the-premises (FTP) services by 2019. Doing so would beat the promise it made to the Federal Communications Commission (FCC) to serve 12.5 million endpoints as part of the approval of the DirecTV acquisition.

The news was delivered during the carrier’s second quarter earnings call by Chief Financial Officer John Stephens. An example of the growth is the recent expansion into the Tulsa area.

According to FierceTelecom, the advantages to the telco extend beyond keeping the FCC happy. FTTP is also a hit with customers. “AT&T expects the fiber footprint expansion to improve churn trends, as it will enable customers to bundle broadband with DirecTV and wireless services.”

USB Soon to Double Speed

The situation is confusing, but the bottom line is that USB is on the road to doubling its speed. CNET reports that USB 3.2 will offer as much as 20 Gigabits per second (Gbps), which is double the current highest speed. The catch is that the USB Implementers Forum is not quite to the point of claiming that speed capability.

The higher speeds, whatever they tap out at, are dependent upon using new hardware. USB-C, which is the family of cables and connectors, that will hit the market in 12 to 18 months.

Google to Help AI and Machine Learning Startups

Google this week launched an extension to Google Developers Launchpad Studio. The new arm will help machine learning and artificial intelligence (AI) startups.

Yossi Matias, Google’s vice president of Engineering, wrote that participating companies will have access to applied AI integration toolkits, product validation support, and access to AI experts, practitioners and investors. The initiative will be headquartered in San Francisco, with plans to expand “activities and events” to Toronto, London, Bangalore and Singapore.

Facial Recognition Rolls Out in Georgia

Those who fear Big Brother have a lot to worry about these days. A good example is occurring in the nation of Georgia. NEC Corp. is rolling out NeoFace Watch, a facial recognition system, across capital Tbilisi and other major cities.

This is one of those ideas that sounds great if crime is a problem and scary if used on innocent people:

The surveillance system uses NEC’s facial recognition software for video called NeoFace Watch, which checks images captured by CCTV cameras in real-time against offender databases and “watch lists” for faster detection of “suspicious individuals”.

Four hundred cameras have been distributed so far. The Ministry of Internal Affairs of Georgia promises “tens of thousands” more. NeoFace is also used by the South Australian government.

Mitel to Acquire ShoreTel

The landscape of unified communications as a service (UCaaS) vendors is getting a bit smaller as Mitel bought rival ShoreTel for $430 million.

The combination of the two companies, if the deal closes, makes Mitel the second largest company in the sector, says TechCrunch. The combined revenue of the two companies is $263 million. Synergy Research says that the value of the market is about $4 billion. The category is expanding as unified communications transitions from premise-based equipment to cloud-provisioned services.

Carl Weinschenk covers telecom for IT Business Edge. He writes about wireless technology, disaster recovery/business continuity, cellular services, the Internet of Things, machine-to-machine communications and other emerging technologies and platforms. He also covers net neutrality and related regulatory issues. Weinschenk has written about the phone companies, cable operators and related companies for decades and is senior editor of Broadband Technology Report. He can be reached at cweinsch@optonline.net and via twitter at @DailyMusicBrk.

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Education becomes more virtual with each passing day. The challenge is that it’s often difficult for instructors to gauge the real level of engagement being achieved. To provide more insight into that virtual education experience, Lenovo is employing facial recognition technology within a Lenovo AirClass virtual training software to provide instructors with scores about the level of engagement any student is having with a piece of content.

Lenovo AirClass now tracks 68 facial features to gauge the level of student engagement. That data is fed into an analytics application that then generates an engagement score.

Rick German, general manager for Lenovo Software, says Lenovo is working toward, at the very least, putting the virtual learning experience on par with being in a live classroom.

“We think it can be equal to or even better,” says German.

Priced at $69 per user per month, Lenovo has also added a replay and break-out room capabilities along with an ability to annotate white boards.

Whether it’s a school classroom or a facility that specializes in corporate training, the whole physical requirement of having to move people from one location to another to learn a skill is one of the primary reasons there is such a skills shortage in the first place. Of course, organizations that specialize in teaching have a lot invested in physical locations such as campuses. But as the rate at which individuals need to acquire new skills or update their existing ones continues to increase, the practicality of sending people off to school to learn a specific skill is in many cases simply no longer practical.

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Lots of things are scary on the Internet, from the Heartbleed vulnerability to the ability of hackers to flip switches on the power grid. Put facial recognition on that list.

Actually, it’s been on many people’s list for a long time. It now is growing to a level of sophistication that should get the attention of folks who haven’t focused on it yet. It isn’t quite there, but we’ve seen how this sort of thing plays out: A technical advance seems vague and nebulous one day and, seemingly overnight, the industry sees the potential benefits and makes it a reality.

This has some folks worried, including Dr. Joseph Atick, a pioneer of the technology, about whom Darlene Storm wrote at Computerworld:

Atick used the NameTag app as an example of face-matching technology being taken too far. The app offered Google Glass users “real-time facial recognition” by matching a stranger with everything about them that can be mined through social media such as their name, occupation and even real-time access to their Facebook, Twitter or Instagram accounts.

It is impossible to argue that the technology is not moving forward quickly. Grant Hatchimonji at CSO offers a very interesting post on where facial recognition is today. It actually turns out it is in different places, depending on whose face is being recognized. In cooperative environments – when folks are willing to look straight into a camera, as at a security checkpoint – the science is moving along well. In uncooperative situations – perhaps ferreting out a known cheater via an overhead camera at a casino – the challenges are understandably more difficult.

The scariness, touched with a bit of weirdness, even extends into the past and future. The Atlantic asks the question of whether computers can use images of children to identify them years later. This is not an unimportant issue, considering how many pictures of children are on Facebook and, in general, how deeply documented everyone’s lives are today. The answer seems to be that it may be possible, but that it becomes very difficult if the kid is younger than seven years of age due to the level of change a younger face undergoes moving forward.

This all sounds very futuristic. It is. But it also is current. The Guardian discusses some of the available commercial products. The usual names – Google and Facebook, to mention two – have or are near facial recognition-based products. The fear is well stated:

There are, naturally, problems, and most relate to privacy concerns. Although privacy is an issue with every form of data mining, at least online the majority of information absorbed by companies is anonymised. Facial recognition, of course, is precisely the opposite. And since facial recognition takes place in public spaces, it is not even necessary for the person surveilled actively to “opt in” to the service.

Facial recognition is another of the endless list of technologies that straddle the line between the good they can bring and the danger they present. Strong laws are needed, but somehow seem unlikely to be put in place.

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