5 Facial Recognition AIoT Applications You Need to Know
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5 Facial Recognition AIoT Applications You Need to Know


The Internet of Things (IoT) has taken the world by storm, inching into our daily lives through internet-connected devices that make cities, homes, and workplaces smarter. When IoT is paired with Artificial Intelligence (AI) technology, it becomes AIoT (Artificial Intelligence of Things). When AI is embedded into the infrastructure of an IoT device, such as its chipset, it unleashes the power of machine learning. AIoT enables connected devices to perform the desired tasks while simultaneously improving via machine learning. The result is smarter, more efficient operations, and greatly enhanced user experiences.

The partnership between machine learning and IoT to create AIoT has paved the way for many connected innovations. When combined with AIoT, Facial recognition makes a compelling use case for businesses and consumers across industries.

Facial Recognition Brings AIoT to Life

There is immense potential for facial recognition and AIoT, especially with edge-based facial recognition solutions. Edge-based facial recognition is embedded in local IoT devices, without the need for cloud processing. Devices like smart locks, mobile phones, point-of-sale (POS) systems, interactive kiosks, and digital signage are just a few examples. Facial recognition equipped IoT devices run facial recognition with extreme precision in milliseconds. On top of dramatically speeding up the process, the absence of cloud processing addresses data security issues and results in huge cost savings.

The best edge-based facial recognition AIoT solutions are highly accurate, process images and camera feeds quickly, encrypt data for security purposes, and work across hardware, platforms, and programs. With a clear understanding of the benefits and potential of edge-based AI, device makers have been quick to redesign their technologies for these solutions.

AIoT Common Use Cases

AIoT is a transformational technology that can be advantageous for many businesses. Let's look at a few of the most common applications for AIoT today.

1. Access Control and Identity Verification

One of the most common AIoT use cases is access control. Facial recognition can transform access control and monitoring systems for homes, residential complexes, and commercial facilities. The technology enables precise, instantaneous identification of individuals. It then provides contactless access for authorized people, or sends instant alerts if block-listed individuals or intruders are detected. Integration of facial recognition in AIoT devices benefits a variety of outlets and technologies, including smart locks on doors or cabinets, mobile devices, and equipment logins.

Contact us to get an evaluation version and price quote for your business scenarios today!

Integrating facial recognition with employee time & attendance and access systems can streamline clock-in/out processes, reduce errors, eliminate the risk of employees sharing access cards, and monitor for unauthorized entry attempts. Access systems can also use facial recognition to unlock facilities for approved employees during only specific work hours.

Access and identity verification doesn't only protect facilities. Imagine a manufacturing warehouse where equipment can only be used by trained, designated personnel. Although the machinery requires a physical key or numerical code to operate, both pieces of information can be lost or stolen. However, if the machine is equipped with facial recognition technology to grant access to designated personnel, there is less risk of unauthorized operation and more security and control. In addition, if a warehouse manager wants to set rules so that a machine can only be operated during working hours, they can program the AIoT device with those specific conditions.

2. Security and Protection

AIoT-based devices have flooded both residential and commercial markets in recent years as a means to enhance security and protection. Whether offered through complete service packages by providers such as ADT or as devices installed by end users, these solutions integrate AI-based features such as motion and intruder alerts. Within a few hours, users can secure a home or facility and access powerful monitoring features.

3. Smart Signage and Retail Analysis

Another strong use case for AIoT is digital signage and interactive kiosks. Digital signs are increasingly popular in malls and retail stores, providing rich media content that can be refreshed through content management systems to keep customers engaged. It is now possible to embed facial recognition in signage, enabling dynamic content based on factors such as gender, age, and even the mood of the person in front of the sign. With facial recognition, AIoT can recognize an opted-in VIP customer and alert staff to provide a personal greeting. Opted-in clients of loyalty programs can also see fully personalized content based on their previous purchase patterns and other collected data.

By adding product selection and payment capabilities digital signage becomes an interactive self-service kiosk. An opted-in customer can be automatically identified by their face and receive a perfectly tailored shopping experience, including special offers and virtual fittings. They can even use their face to complete their purchase and perform contactless payments.

You can also leverage AIoT devices with facial recognition for data analysis. Devices capture behavior and demographics to determine if customers look confused in certain aisles, or if they are more inclined to smile when viewing certain products. Facial recognition technology can capture these patterns and use AI to perform customer analysis. Retailers can then rearrange their store to produce more positive experiences.

4. Health and Safety

Because of the Covid-19 pandemic, the health and safety of individuals in public and private spaces has become a priority. In some places mask wearing is still required, which makes a compelling use case for integrating AIoT and facial recognition with health kiosks.The kiosks, when equipped with facial recognition, use cameras to detect if an individual is wearing a mask, and if they are wearing it properly — covering the entire nose and mouth. A camera with thermal scanning capabilities can even read body temperature to ensure no one enters a property with a high fever. The kiosk can send an alert to designated personnel to take necessary action if required. It can also ask a set of health questions to make sure an individual is well enough to enter a store or restaurant. Depending on how the user responds, the AIoT device will prompt the next response and provide the appropriate action.

5. eKYC and Financial Fraud Prevention

A cybersecurity use case that is quickly growing in popularity is electronic Know Your Customer (eKYC) authentication. Facial recognition provides the most accurate and convenient technology for two-factor authentication for opening bank accounts, applying for credit, ATM transactions, mobile banking, buying insurance, and remote customer service. The process is as simple as matching the live face capture to a valid ID that is either scanned in the process or already on file.

FaceMe Is Your Answer to Enabling Facial Recognition in AIoT Devices

FaceMe’s edge-based facial recognition solution is easy to integrate across a wide range of devices, offering one of the most comprehensive chipsets and OS support. The highly accurate AI engine is ranked one of the best in the NIST Face Recognition Vendor Test (FRVT). FaceMe can be deployed across a wide range of scenarios, including security, access control, public safety, smart banking, smart retail, smart city, and home protection.

For an in-depth look at other use cases, view our Top 7 Use Cases in 2022

The Future of AIoT and Facial Recognition Is Bright

Facial recognition is the future of AIoT technology. It makes AIoT solutions safer, smarter, and more human. However, there are barriers to reaping its full potential. Some of these barriers are physical, such as hardware. Others are social, including privacy and data security concerns.

When considering physical barriers, it is important to remember that there is no one-size-fits-all approach. Businesses have varying needs, as well as budgets, and should look to solutions that will work for them. The best facial recognition solutions can be scaled up or down to suit the specific needs of a business and its use case.

It is also important to ensure physical environments are conducive to facial recognition technology. This means paying attention to factors that can affect accuracy, such as lighting, camera position, and lens cleanliness.

In terms of social barriers, facial recognition has been viewed as a privacy concern. This does not mean the technology should be abandoned. Rather, we need to clarify exactly how facial recognition works and ensure ethical regulations are in place.

We're committed to innovation, to delivering solutions that businesses need and consumers are comfortable with, and to creating safe, contactless environments and amazing user experiences.

We're excited for the future of facial recognition.

For a full overview of facial recognition, how it works and how it can be deployed, read Edge-based Facial Recognition - The Ultimate Guide.

For how is facial recognition used in 2022, read Facial Recognition – How is It Used in 2022?

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