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Use cases for the future

Facing the Future: Emerging AI Facial Recognition

Serhii Pospielov
#AI
© Shutterstock / Vitaly Art

With new technologies like AI, opportunities have emerged to adopt facial recognition in a variety of different domains. Today, there are many success stories where AI has provided reliable and accurate face recognition technology. This article examines some of its potential use cases.

Facial recognition technology is a rapidly growing multi-billion dollar industry. Estimates indicate that 74 percent of hotel operators foresee mainstream adoption of facial recognition and biometric technology in the hotel industry alone by 2025. This rise in adoption rates is attributed to more recent advancements in Artificial Intelligence (AI) – as it wasn’t always feasible for this technology to thrive across industries.

While the concept of facial recognition has been around for many years, it has taken time for widespread adoption to occur due to concerns around reliability. Industries such as financial and security cannot afford the costly mistake of a facial recognition error. With new technologies like AI, opportunities have emerged to adopt facial recognition in a variety of different domains. Today, there are many success stories where AI has provided reliable and accurate face recognition technology. According to the National Institute of Standards and Technology (NIST) the best facial recognition technology had an error rate of only .08 percent as of April 2020.

AI Options for Facial Recognition

The rise of AI was the catalyst for the continuously growing variety of tools for facial recognition. It’s important to know the resources and understand what’s available today. Some technologies can be quickly deployed without machine learning expertise, while others require a deeper expertise and understanding of the technology. Additionally, price ranges can vary quite widely from free solutions to the more expensive options costing tens of thousands of dollars per year. The most popular include AWS Rekognition and Azure Facial Recognition services. These tools are easy to start and they integrate services that require payment for each recognition.

The downside of using cloud providers is that sensitive information is sent to the cloud and the end user has little to no visibility into what they do with the information. As an alternative, many free and open-source facial recognition software options have emerged, such as CompreFace or FaceNet. Unlike cloud services, the individual or organization will need to deploy and manage the tools. However, where security is a concern, this method allows the end user to store and process their data in a safe way.

AI Facial Recognition Growth Markets

The future outlook for AI-based facial recognition offers a variety of applications across a wide range of potential growth markets. One application for this technology is in attendance, whether for schools and universities or in workplaces. At most organizations, attendance requires manual intervention of some form, whether that is on the part of the employee who must manually check-in when they arrive at work, or in schools, on the part of the teacher who needs to call for attendance to ensure every student is present. Facial recognition systems give the opportunity for automation of this previously manual task, allowing employers and teachers to save valuable time and ensure increased attendance accuracy.

Another potential growth market for facial recognition technology is in the field of marketing and personalization. Imagine a scenario where retail stores can have additional data on the products their customers are browsing and spending the most time with. Today, online eCommerce organizations are already able to track that data and use that information to serve up suggested or related products, offer targeted discounts and promotions, and even create bespoke online experiences for specific customers. With facial recognition technology, retail spaces could mimic at least some of this experience, offering real-time discounts or changing electronic displays to appeal to specific customers as they move through the store. Another potential use case for facial recognition is to help create VIP experiences for key customers. When a customer known to make frequent purchases enters the store, staff could be alerted to their presence and be prepared to create a “VIP experience” for them.

One additional use case for facial recognition comes from the hospitality industry, it could be used to streamline the check-in and check-out processes. For example, when a guest arrives at the hotel, facial recognition could bring up their details and automatically check them in. More simply, the software could automatically bring up their file and information for the hotel employee to create a more personalized and streamlined experience.

A decade ago, before AI had really emerged as a technology of the future, facial recognition was mostly conceptual. However, the advancements in artificial intelligence and machine learning make it possible to apply facial recognition in a wide variety of applications to create efficiencies that not only reduce the manual workload for employees, but create a convenience that improves the customer experience. Accuracy rates have improved dramatically with AI facial recognition, and with advancements in the technology we anticipate even more reliable solutions in the future. As more applications and use cases emerge, the AI face recognition market will continue to expand in 2022 and beyond.

Author

Serhii Pospielov

Serhii Pospielov is AI Practice Lead at Exadel (www.exadel.com). Serhii has more than a decade of developer and engineering experience. Prior to joining Exadel he was a game developer at Mayplay Games. He holds a Master’s Degree in Computer Software Engineering from Donetsk National Technical University.

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