Put an End to False Alarms

Historically, facial recognition systems used computer algorithms to pick out specific, distinctive details about a person’s face. These details, such as distance between the eyes or shape of the chin, are then converted into a mathematical representation and compared to data on other faces collected in a face recognition database.

But how this process is actually performed varies widely by solution provider, so it’s important to understand how a vendor performs facial recognition in order to assess whether it’s following ethical best practices. More recently, leading solution providers leverage neural networks to recognize specific faces based on deep learning. These networks assign a unique mathematical vector to a specific person’s face.

In the real world, say within a casino or stadium, subjects are far less cooperative and do not look directly at the camera. In these real-world scenarios, we often encounter extreme poses and camera angles, poor image quality, low lighting, or occlusions such as hats, masks, or other coverings.

Neural networks trained under these conditions perform much better in production than models that were built under ideal circumstances – that is, where the person is looking directly at the camera, the camera is at face level, the image quality is crystal clear, and the subject is well lit.