With the global pandemic accelerating the implementation of new technologies to protect from the spread of the virus, the use of Facial Recognition in stadiums, cities, and all types of facilities has become the center of discussion in many circles. Of course, Facial Recognition is a broad term that only describes the function of the technology, not the end goal or use purpose. With so many use cases now being widely applied in the FR industry, the distinction between the purpose and end goal has become essential. It not only describes the way the technology can be used but also how it benefits consumers.
First, we have Facial Detection, the broadest term. Here we simply see the use of a computer-vision algorithm that can say “that is a human face,” when presented with imagery or videos containing people. While this is the base technology used for all other applications, Facial Detection can be used on its own for a variety of purposes. For example, facial tracking and facial analysis, which can determine the effectiveness of targeted marketing by tracking users who view campaigns (such as Wicket’s very own Audience Technology).
Then, we have Facial Recognition. Facial Recognition uses a 1:N (one-to-many) mechanism, or in other words, compares and matches a single face in a crowd (whether it be a live video feed, a photo, or pre-recorded footage), to a human face that has been provided to the computer. To further simplify this, Facial Recognition uses a camera to run an image through a large database of faces to look out for. The use cases for this are quite easy to see from the description alone: surveillance and security, finding individuals in a crowd, whether for security reasons (such as to protect an establishment or an individual) or for Management reasons (to ensure a particular guest receives five-star treatment.) Of course, this technology can be implemented retroactively to aid in investigations, too.
Finally, we have Facial Authentication, the cornerstone of Wicket. Facial Authentication is a 1:1 (one-to-one) mechanism. This means that Facial Recognition works to exclusively match the face in the camera feed to a previously provided image from a user of the authentication tech. Not only does this protect individual user privacy by requiring opt-in, but the format reduces the risk of false positives and false negatives by nature—since the source image has to be a high-quality, clearly visible image of the user. This has a variety of use cases, from guest management in a residential or commercial building, to securing a sensitive area using two-factor authentication with Facial Authentication, to time-logging employees and minimizing the risk of piggy-backing in the workplace.
Ultimately, it may be easier to call all of the facial technology by the same name, but providing adistinction allows for clear communication with end-users, guests, and customers, and a better grasp on how we can best serve them.