Facial recognition technology continues to evolve at an astonishing pace, largely propelled by significant gains in Artificial Intelligence (AI). Cutting edge facial recognition systems can accurately determine age, gender, ethnicity, and authenticity, regardless of lighting or physical obstructions such as helmets, glasses, or face masks. These systems are also more attainable than ever, with a lower financial bar to entry that makes facial recognition indispensable for businesses of all sizes, in all industries. The question is no longer, "does my business need a facial recognition system?" but rather, "which system is right for me?"
Choosing a facial recognition system requires careful consideration of several crucial factors. In this guide we outline the key factors that all businesses should consider when integrating facial recognition technology:
Facial recognition technology detects faces, extracts features, and creates a facial template to compare against an existing database to verify a person’s identity. It is becoming increasingly more commonplace in our everyday lives.
There is a tremendous variety of facial recognition use cases, such as access control, surveillance and security, time and attendance, and banking. You can find out more about the top 7 use cases of facial recognition in our accompanying articles.
Facial recognition technology can be deployed in both edge and cloud environments. Edge devices include smart locks, self-service kiosks, and mobile banking apps. Some benefits of deploying facial recognition at the edge are lower cost of ownership, faster response time, and better service availability. If you are interested in deploying facial recognition at the edge, you can read What is Facial Recognition – The 2023 Ultimate Guide for Facial Recognition Technology.
The most important consideration for deploying facial recognition is whether or not you can utilize in-house expertise (e.g., a software engineer) to develop the solution from scratch. If not, you can use a solution like a facial recognition SDK. To learn more about which facial recognition solution is right for you, check out the best facial recognition software solution – FaceMe.
There are two types of facial recognition accuracy:
Accuracy is a critical aspect of a facial recognition system because it protects and monitors access to secure facilities, confidential data, and even controlled substances. The most accurate facial recognition algorithms require substantial storage and processing power, significantly increasing the total cost of deployment. For this reason, we recommend considering solutions from vendors who regularly update algorithms and are vetted and ranked highly in industry testing such as those defined by NIST.
Some of the most popular use cases for facial recognition will not require a 99% level of accuracy, but we recommend utilizing solutions that perform at no lower than the 95th percentile.
Accuracy Requirements by Use Case
More important
Less important
Bank security access:
Protects valuable financial assets, leaving no room for error
Stadium turnstiles:
Requires a moderate degree of accuracy so that people don’t need to make multiple entry attempts. Flow of movement and reliable hardware are more critical aspects
Accuracy Requirement by Vertical
More important
Less important
Large-scale smart factories:
High liability scenario; protects personnel and equipment
Single Smart Retail Store:
Lower liability with a smaller quantity of visiting customers and smaller number of VIP or block listed individuals to be detected
Accuracy Requirement by Deployment Scale
More important
Less important
Large department stores:
Need to identify VIP and block-listed customers across a sizeable national database
Local shops:
Fewer customers and smaller database
Each facial recognition solution offers specific features, but every solution should provide three essential components:
Advanced facial recognition systems such as FaceMe, also include enhanced features:
Features by Use Case
Advanced
Basic
Access control for a secure warehouse:
Anti-spoofing ensures spoofers cannot use photos/videos of approved personnel to bypass the system
Recognition for retail loyalty programs:
Anti-spoofing is less critical as the likelihood that individuals would try and spoof the system for a low value transaction is very low
Features by Vertical
Advanced
Basic
Smart city:
Mask detection features are necessary for public health and safety, especially during the pandemic conditions
Smart home:
Mask detection is unnecessary as individuals typically do not wear masks in their own homes
Features by Deployment Scale
Advanced
Basic
Shopping mall:
The ability to detect multiple faces concurrently is critical when scanning large groups for block-listed individuals
Individual employee entrance:
When used for identity verification and employee clock-in/out, only one individual is scanned at a time making multiple face detection unnecessary
As with accuracy, many factors affect the performance of a facial recognition system. Let’s break them down:
To achieve the best performance, you will also need optimal chipsets and software for your specific scenario.
Performance is critical in multiple use cases. For example, deployments in larger facilities often need hundreds of video channels running concurrently. High-performing facial recognition models can significantly reduce the number of expensive workstations required to monitor such facilities.
The following section compares edge and cloud architecture for facial recognition systems. Edge systems generally perform facial recognition much faster, as sending images or video to the cloud for processing increases response times – from milliseconds to several seconds.
Features by Use Case
More important
Less important
Airport monitoring:
Must identify and detect hundreds of faces concurrently, requiring more powerful hardware to process enormous volumes of data simultaneously
Library check-out:
Scans individual faces at check-out, so performance is less critical since the system only performs one facial match at a time
Features by Vertical
More important
Less important
Warehousing/logistics:
Many individuals in a large facility with multiple camera feeds requires high-performing facial recognition
Small offices:
Processing one or two faces at a time, in a facility with feweraccess points , requires less performance power
Features by Deployment Scale
More important
Less important
Large facility with multiple video feeds:
Additional video feeds affect processing time and performance and require higher-performance chipsets and software
Small facility with one video feed at the entrance:
Single video feeds do not affect system performance, so performance is not likely to be a top factor when selecting system components
Whether edge or cloud-based, architecture impacts the security and performance of your facial recognition system. This means it is an essential consideration for operators seeking maximum speed. Edge-based systems operate faster because information does not have to be sent back and forth to the cloud, usually adding several seconds of transmission time.
Edge-based systems offer significant benefits:
However, the cloud can be a better option for use cases with specific characteristics:
When selecting a facial recognition system, hardware is sometimes a constraining factor. Thanks to evolving innovation in hardware and chipset technology, there are ever-increasing device options on the market to best address speed, power, form factor, and cost constraints. These innovations have enabled many new use cases for facial recognition that were previously impossible.
Depending on your cost and performance needs, various chipsets can run facial recognition. A summary follows, but read our accompanying article for more in-depth coverage.
Features by Use Case
Higher-performing
Lower-performing
College campus health and security desk:
Monitoring security and mask usage in a pandemic across a college campus requires hundreds of cameras and powerful hardware such as workstations or servers
Apartment building smart locks:
Integrating facial recognition to individual door locks (smart AIoT devices) would be more dependent on form, and less constrained by performance
Features by Vertical
Higher-performing
Lower-performing
Hospitals:
Running multiple video feeds to simultaneously verify identity, and mask-wearing for security access control, and health monitoring uses, requires more robust hardware such as a workstation
Individual retail store:
A smaller-scale application running fewer video feeds should prioritize cost and convenience over performance when selecting hardware. A PC would be the most appropriate option
Features by Deployment Scale
Higher-performing
Lower-performing
Nationwide chain of retail stores:
Monitoring hundreds of video streams and photos from IP cameras across multiple stores is a large-scale application that requires a hybrid approach. High-performance workstations in each store perform face detection and extraction and combine with centrally-located, high-powered servers to match captured facial templates with a central database
Individual hotel:
Implementing a facial recognition system for an individual hotel places less pressure on performance: a PC or workstation are both appropriate
Facial recognition software processes information extracted from video feeds to detect faces and determine matches. Let’s compare the two approaches available, plug-and-play solutions, and software development kits (SDKs).
Plug-and-Play Software
Until recently, facial recognition technology solutions existed only in the form of a software development kit (SDK). SDKs are generally flexible and facilitate perfectly tailored solutions, but they require significant programming and integration work. Plug-and-play software is now available on the market, offering a quicker deployment timeline in specific and well-defined use cases. Software solutions like FaceMe Security are preconfigured to target typical security scenarios including access control and monitoring.
Plug-and-play solutions have the software infrastructure needed for easy implementation. They are highly scalable, and can be deployed in single-camera, multi-camera, and multi-location scenarios. They can connect with existing cameras and networks, and superior solutions can even connect with other systems such as VMS, door locks, time and attendance software, and more.
Software Development Kits (SDKs)
SDKs are highly flexible for unique scenarios where you want complete control of the facial recognition algorithm. SDKs allow organizations to leverage facial recognition in existing workflows and processes, but it’s important to note that you will need robust internal computing and substantial IT talent to integrate the SDK into your existing software infrastructure.
Features by Use Case
SDK
Plug-and-play
Patient management and access control in a hospital environment:
This type of facility depends on a series of uniquely designed processes and systems, each often running on its own platform, so an SDK would be a more flexible software format
Retail store, or a chain of standardized stores for security and access control:
If there is an existing security system or video management system (VMS) connecting cameras, then a plug-and-play solution is attractive as it requires minimal deployment time, is cost-effective, and needs little to no maintenance
Features by Vertical
SDK
Plug-and-play
Retail banks:
If a bank wants to change its entrance readers from credit card scanners to facial recognition, they will likely incorporate it into existing systems and enterprise infrastructure, so an SDK would be a better fit
Security and access control in a large office building:
The building will likely already have a VMS with security cameras and door access control, making it easy to connect with a leading plug-and-play option
Features by Functionalities Required
SDK
Plug-and-play
SDK allows you to build the solution with all desired functionalities without limitation.
Plug-and-play solutions address a relatively standard set of functionalities or use cases
Before integration it is wise to consider costs for the lifespan of your facial recognition system. Some of the core components are:
Here are some examples of costs relative to deployment size, in increasing order:
TYPE OF COST
SCENARIO
Small shop, single location
Chain of small shops, multiple locations
Large facility, single location (e.g., factory)
Large facility, multiple locations(e.g., national grocery store chain)
Software
Small shop, single location
Low:
Plug-and-play software tied to a low number of video feeds
Chain of small shops, multiple locations
Higher:
Driven by multiple video feeds
Large facility, single location (e.g., factory)
Minimal (software-based) to potentially significant (SDK-based):
Installation costs and billing are based on the number of video feeds; deployment costs can be high
Large facility, multiple locations(e.g., national grocery store chain)
Minimal (software-based) to potentially significant (SDK-based):
Installation costs are based on the number of locations and size of deployments. Costs typically grow with the number of locations, although you may receive discounts on bulk orders; deployment costs can be high
Hardware
Small shop, single location
Low:
Less expensive hardware needed, often a reasonably performing PC
Chain of small shops, multiple locations
Relatively low:
A PC or low-cost specialized computer (e.g., NVIDIA Jetson) and a few cameras at each location; at least one server if there is data on customers, employees, or block-listed people
Large facility, single location (e.g., factory)
Higher:
One or more workstations with multiple GPUs or VPUs, paired with sizable camera deployment at that one facility
Large facility, multiple locations(e.g., national grocery store chain)
Higher:
One or more workstations with multiple GPUs or VPUs, paired with sizeable camera deployments at each facility. In addition, each location or regional center will probably need one or more servers for database hosting and sharing
Integration and training
Small shop, single location
Low:
For plug-and-play software, this is often included in packages offered by the VAR that sold the system
Chain of small shops, multiple locations
Reasonable:
Driven by the number of areas/shops and geography covered
Large facility, single location (e.g., factory)
Moderate to high:
Requires learning a more complex system
Large facility, multiple locations(e.g., national grocery store chain)
Recurring:
May need qualified, outsourced trainers visiting each location on a rotating basis
Recurring
Small shop, single location
Minimal Monthly
energy costs
Chain of small shops, multiple locations
Relatively low
Monthly energy and bandwidth costs are driven by the number of locations
Large facility, single location (e.g., factory)
Monthly
Monthly energy costs as well as potential monthly maintenance with integrator or VAR
Large facility, multiple locations(e.g., national grocery store chain)
Significant Bandwidth and energy costs; likely has a monthly maintenance contract with integrator
A smart approach starts with familiarizing yourself with the broad range of options available for designing a facial recognition solution, and then determining which would be best suited to your unique scenario, understanding that each decision you make will impact the solution’s effectiveness in meeting your needs. Since technology and solutions are improving exponentially, learning about new trends will inform your testing and deployment timeline. You can then focus on the more crucial factors for deployment success, such as performance, features, hardware, etc.
It can also be helpful to learn about successful facial recognition deployments and failures in your industry. Many industries have associations and task forces to help members monitor and analyze facial recognition technology.
To learn more about choosing the right facial recognition system for your company, please visit the FaceMe® official website or contact our sales team today!
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