From smartphone logins and personal banking verification through to playful selfie filters, the capabilities of facial recognition have made a speedy transition into the heart of modern UX. Client service functions are key use case in which we’ve seen rapid adoption, and the market is only set to grow - a study published in June 2019, estimates that by 2024, the global facial recognition market will generate $7 billion of revenue. However, for this technology to be rolled out at scale a number of hurdles are still to be overcome.
Security is a universal concern and it’s no secret that human error, fraud and corruption all play a part in any system being able to ensure high levels of reliability, which is why many organizations are exploring other methods of authentication. Biometric based authentication uses AI to recognize unique biological identifiers, such as fingerprints or irises, to create a more foolproof security method. However, within this field, facial-based authentication demonstrated the highest reliability rate when tested against other office entrance applications, boasting a 93% success rate.
As exciting as this sounds, organizations that wish to roll out a facial-based ID system are still facing some hurdles on the road ahead. The quality of the data sets and the APIs which operate the framework can have a major impact on reliability rates. One trial led by the South Wales Police in the United Kingdom staggering 92% error rate when matching football match attendees to a criminal database. In addition, the widespread adoption of the technology at sites such as offices, apartment complexes and public spaces is dependent on some hefty hardware to ensure that software functions in near real-time. As Edge AI capabilities grow more sophisticated, the capabilities of ‘plug-and-play’ IoT devices are unfortunately no longer sufficient. For facial recognition to work in such busy locations, small yet powerful devices with a GPU capable of running AI/ML models, 3D and depth perception cameras, an efficient cooling system and, crucially, connectivity back to the master cloud are required.
Let’s explore how more reliable software can be developed and how the introduction of intelligent edge computing has the power to make this technology a viable solution for security requirements.