Businesses are using faces to gain security and business insights
Smartphones and social media all utilize facial recognition, and user acceptance has paved the way for more varied uses. While the technology is still mostly associated with security, government and law enforcement, businesses are taking advantage of it as well for both security and business intelligence purposes.
The overall global facial recognition market is growing, expected to reach nearly US$7.8 billion by 2022 at a compound annual growth rate (CAGR) of 13.9 percent, according to a report by MarketsandMarkets. The report cites the growing need for surveillance in public spaces as a major contributor to growth.
The unique capabilities of advanced facial recognition can be applied to almost any vertical. Also, since the technology requires no physical contact or credentials that can be lost, stolen or replicated, it is an attractive solution for many different applications. Add in the insights businesses can gain from its ability to easily identify and track individuals — which could provide them with valuable data to perform advanced trend analysis, productivity research workflow processing and so much more — it is no wonder more and more businesses are deploying facial recognition solutions.
Making Businesses More Efficient With Facial Recognition
Businesses are deploying facial recognition technology to increase operational efficiency and learn more about customers.
Nowadays everyone is using facial recognition and businesses are no exception. The use of facial analytics solutions to gather information on demographics and consumer buying patterns is expected to grow significantly in the coming years, according to a report by MarketsandMarkets. Adoption of face recognition technology is growing across industries, which is being fueled by growing awareness, advancements in the technology, and accessibility. For instance, the development of advanced facial recognition analytics that are centralized has made it relatively easy to apply to any networked camera, explained Shawn Mather, Director of Sales for the U.S. at Intelligent Security Systems (ISS).
With businesses also looking for more ways to utilize smart technologies, and with artificial intelligence (AI) and deep learning gaining momentum, the use cases for facial recognition have opened up. Additionally, improvements in video surveillance cameras have allowed “organizations to realize the full value of video surveillance investments,” according to Stephanie Weagle, CMO of BriefCam.
“The growing sophistication of video content analytics (VCA) systems combined with the market’s fuller understanding of the ways in which video analytics solutions can drive organiza- tional efficiency and performance alongside security, has resulted in businesses of all types taking a deeper interest in VCA and face recognition,” Weagle said. From a retail perspective, leveraging facial recognition technology allows them to better understand customer demographics (e.g., gender, age, etc.). This data provides the retailer with a better understanding of who their customers are, which could enable them to better personalize the shopping experience and tailor their marketing strategy.
“When companies educate themselves on demographic composition, they create business intelligence opportu- nities to improve convenience in how people interact with their built environ- ments and to enhance the personal- ization of experiences in advertising,” explained Dan Grimm, VP of Computer Vision and GM of SAFR at RealNetworks. Grimm explained that companies can use facial detection and charac- terization, which does not retain any biometric information, to gain actionable insights of their customers. “For example, shopping mall owners will be able to make better advertising, leasing and customer service decisions if they know that between the hours of 12 p.m. and 1 p.m. they tend to see traffic of X number of persons broken down by 60 percent female, 40 percent male, with an average age of 42, based on a single IP camera properly tuned for an entrance,” he added.
Businesses are also using facial recognition to create customer loyalty programs to help identify VIPs.
Mather also highlighted the use of facial recognition in workforce management. This is a trend he sees on the rise globally. The ability to accurately identify and track personnel for time and attendance management with facial recognition is especially being sought after. Facial recognition is being deployed for this purpose in a large number of industries where large, but often temporary workforces are being deployed to either permanent or temporary sites, he said. “Perfect examples are factories and construction sites, where shifts change based on delivery schedules and production lines, and different groups of specialized workers need to be on production lines at different intervals,” Mather explained. These are just some of the ways businesses are using facial recognition technology for nonsecurity purposes today. In the near future, many expect facial recognition to become more mainstream where its use for even more business intelligence operations will continue to grow.
How Businesses Use Facial Recognition to Enhance Security
Facial recognition is helping businesses stop thefts and keep environments more secure.
More and more businesses are using facial recognition technology to improve situational awareness. Doing so gives businesses a smarter, easier way to monitor who is entering, staying and leaving their environment, while also increasing safety, security and convenience. Doug Aley, CEO of Ever AI, noted that “mission-critical” applications form about 75 percent of the facial recognition market. This includes use cases such as security surveillance, access control, digital authentication and government and law enforcement applications. According to a report by MarketsandMarkets, the increased need for enhanced surveil- lance and monitoring at public places and the increased use of the technology in industries such as the government are driving the market growth. While the main purpose of deploying a facial recognition solution is security, it is also about “empowering individuals to use their faces as a token to gain fast safe access and authen- tication,” said Dan Grimm, VP of Computer Vision and GM of SAFR at RealNetworks.
Facial recognition is “ideal” for environments that need to positively identify individuals for physical and data security clearance, general access permis- sions, compliance with mandated regulations, and financial verification, according to Shawn Mather, Director of Sales for the U.S. at Intelligent Security Systems (ISS).
In a business environment, such as retail, “this could mean leveraging footage of different shoplifting incidents, assembling a suspect watchlist using frames from video surveillance, and then responding to alerts triggered by a video content analytics (VCA) system’s detection of a biometric match for the suspected shoplifter,” explained Stephanie Weagle, CMO of BriefCam. From there, security or police officers could investigate further to determine whether the alert has correctly identified the shoplifter and, if so, apprehend the perpetrator before the store bears further losses.
Facial recognition can also be used to provide live analytics for industries where there are high numbers of unknown visitors (e.g., malls, retail spaces, event venues, stadiums, etc.). This also applies to large enterprises with high visitor flows, such as hospitals, universities and stadiums, that have a need to know when persons of interest appear on camera. Grimm used a sports stadium as an example. Stadium operators might flag banned patrons in its database, while box owners may want notifica- tions when VIPs are onsite, in order to properly greet them and provide a superior level of customer service. “To support effective ‘watchlists’ — both for threats/concerns and for VIPs — facial recognition systems must be adept at high accuracy under the real-world conditions of identifying people in live video. This means avoiding false positive matches and reliably identifying people despite variations in lighting, orientation and facial occlusions due to scarves, glasses and hats,” Grimm explained. Customers from schools, office buildings and manufacturing are also using facial recognition to provide secure access to facilities. “In these cases, facial recognition systems enhance security in ways that are superior to badges, which can be easily stolen, and also offer features that catch piggybacking instances to offer a more accurate assessment of who is entering and exiting,” Grimm explained.
Facial recognition can also enhance convenience over existing access management solutions. For example, with facial recognition users no longer have to deal with situations where badges are forgotten at home or left on a desk. Grimm added that for secure access, facial recognition solutions should include anti-spoofing to prevent unauthorized access to those attempting to use a photo to gain entry.
How Businesses Should Protect Privacy When Using Facial Recognition
As facial recognition becomes more ubiquitous, concerns about privacy are at an all-time high.
Facial recognition has been entrenched in controversy lately. San Francisco recently made headlines by becoming the first city in the U.S. to ban the use of facial recognition technology by law enforcement and government agencies; however, businesses are not included in this ban.
Built-in facial recognition in smartphones has helped ease the general population’s concerns over biometrics by making it a norm. However, many civil liberties groups and consumers are still just as concerned about how enterprises are using and storing facial recognition data. The determination of privacy is often dependent on the use case. In some instances, privacy is determined by the governing entity. For example, the Global Entry program, administered by the US Department of Homeland Security, uses facial recognition to verify that the person in front of the camera is the same as the one in the passport photo. “Not only is consent and privacy in this instance ‘implied,’ but also legally mandatory,” said Doug Aley, CEO of Ever AI.
On the other hand, consumer expectations of privacy and consent are often contingent on the application. “We typically find implied consent in situations where consumers are expecting it (e.g., consumers expect that bad actors are not allowed entry into the country, and so the concept of face recognition to identify them is acceptable),” Aley explained. However, there is a delicate balance that will come down to the difference between mission-critical applications (e.g., where the government doesn’t need permission to use an individual’s face) versus general purpose face recognition for casual, entertainment-focused applications, he added.
In terms of legislation, laws regarding privacy are rapidly evolving around the world. In the U.S., states such as Illinois, Texas and Washington have specific biometric privacy laws governing the use, collection and storage of biometric data. In Europe the General Data Protection Regulation (GDPR) also has specific clauses mandating how biometric data can be collected, used and stored. For example, the GDPR states that EU residents must give explicit consent before their data can be collected, and that they have the right to withdraw consent at any given time — this is known as “the right to be forgotten.” Dan Grimm, VP of Computer Vision and GM of SAFR at RealNetworks, believes regulations are needed at a national level in the U.S., not just by jurisdiction. This would help to provide a baseline for how facial recognition can be deployed in ways that take into account the “important missions of our customers and the interest of the general public.”
While making sure all facial recognition deployments abide by privacy regula- tions is a given, whether in the cloud or on premises, businesses can further maintain privacy by doing their part. This should include ensuring that all data is encrypted in transit and at rest; systems are built with stringent cyber protections; providing the ability for individuals to be deleted from a system; and offering an opt-in/opt-out structure that encourages users to provide consent around the use of facial recognition. “For SAFR from RealNetworks, we find this particularly important and not only include these features out of the box, but also provide our customers with best practices for implementing facial recognition,” Grimm added.
From a consumer’s perspective, concerns surrounding facial recognition rests more in the hows (e.g., how it is being used, how it is being transmitted and how it is being stored) rather than the actual use of the technology, according to Shawn Mather, Director of Sales for the U.S. at Intelligent Security Systems (ISS). For this reason, he explained that privacy is much more an issue of application. In the future, we can expect that governments worldwide will continue to develop policies to regulate the use of biometric technologies, as well as define the rights of opting out of being tracked digitally. We may even see more cities opt to follow in the footsteps of San Francisco and ban certain applications of facial recognition technology altogether.
What’s Required for Businesses to Deploy Facial Recognition?
Getting the most out of facial recognition requires the right equipment; however, needs may vary by application.
The hardware needs of businesses wanting to deploy facial recognition can vary depending on the application. Not every situation requires the highest resolution camera or the highest computing power, nor does every every environment pose the same challenges (e.g., lighting, crowding, weather, etc.). Generally, in order to deploy a facial recognition system what is needed are a well-tuned camera, local computing power, and software. Hardware systems must be paired with the appropriate computing power to run facial recognition efficiently, which depends on whether you are managing a high- or low-density environment.
However, hardware requirements can vary greatly depending on the application and deployment architecture. For example, secure-access use cases, where you are viewing a few faces at a given time, can leverage lower-resolution cameras with lower frame rates and require less computing power (in addition to deploying fewer cameras), which effectively lowers your total cost of ownership (TCO), explained Dan Grimm, VP of Computer Vision and GM of SAFR and RealNetworks.
On the other hand, when using watchlists, deploying more cameras can improve accuracy and performance. Grimm added, “If the facial recognition platform supports a distributed architecture by doing detection at the edge and recognition in the cloud, then you’ve not only lowered TCO, you’ve also increased your ability to scale in a massive way.”
In the early days of face recognition, there was often a tradeoff between accuracy and device power. “Lower powered devices, either in terms of chipset, bandwidth requirements or camera resolution, suffered from lower accuracy,” noted Doug Aley, CEO of Ever AI. Today, Ever AI has had success in being able to deploy on everything from a single core legacy processor all the way up through a cluster of high-powered GPUs, like an NVIDIA T4. “There are now other companies like ours where the accuracy tradeoff is no longer an issue,” Aley added. Nowadays, speed is where the major variability comes in — the more powerful the hardware, the faster the speed of matching and the faster the overall user experience. Aley explained that most modern chipsets, especially from a quad-core onward, are going to be very fast. Furthermore, today’s face recognition models, and the frameworks off which these models are built, are getting more adept at handling lower-power chipsets. Shawn Mather, Director of Sales for the U.S. at Intelligent Security Systems (ISS) highlighted software integration issues over complications with hardware. Software providers, however, can overcome these challenges by making their solutions compatible with VMS solutions and electronic access control solutions. The type of facial recognition — 2D or 3D face recognition technology — a businesses chooses to deploy may also come with its own specific set of challenges and requirements. A report by MarketsandMarkets noted that captured images from earlier 2D facial recognition technology were highly dependent on illumination, meaning poor lighting significantly affected image quality. Another challenge was the “incompatibility of integration between software tools and biometric hardware devices.”
However, the report expects 3D technology to have the largest market share in the coming years. Unlike 2D technology, 3D technology is not dependent on illumination. This enables it to capture higher-quality images in uncontrolled environments, such as poorly lit or completely dark areas. Something else to consider in the years to come are facial recognition cameras, where the recognition process is done on-board at the frontend. These types of cameras, though, require strong computational power since all of the tools for recognition are on-board. While several camera companies are developing face recognition cameras, the overall market is still in a fledgling state, but may be something to look forward to in the future.
