Plumerai Familiar Face Identification¶
At Plumerai we’ve built the world’s most accurate familiar face identification that is tiny and fast. It identifies people with a high accuracy in any situation, and runs at the edge or in the cloud.
You can try our AI live in the browser with your webcam.
We have split the technical documentation for our familiar face identification products into two sections:
For information about tracking, robustness, camera support, privacy, and lighting, distance, mounting, and location requirements, see the People Detection Overview.
Familiar face identification features¶
State-of-the-art face identification¶
Our solution uses an end-to-end deep learning approach that consists of three neural networks: one for object detection, one for face representation, and one for face matching. We have applied various advanced model design, compression, and training techniques to make these networks fit within the hardware constraints of small CPUs, while retaining excellent accuracy.
Our algorithm supports enrollment of new faces in the face library using the same API. Furthermore, it supports loading and storing a face library. The storage requirement for an enrollment of a single user is very small.
Our algorithms assign identities to person boxes, but it does so using face detections. When it can uniquely match a face to a person, the identity in the person box should match that of the matched face box. However, the algorithm can’t always be certain which face belongs to which tracked person. Tracking people is also more reliable than tracking faces, since people’s faces can for instance be turned away from the camera for a moment. Therefore, it is common that the identity of the person is known even when the identity of a face is unknown. Also, a new face might be associated with an already identified person. The identity of that person will then be determined by the information of both faces that were associated with it, and the strongest match will be selected.