Dummy vectors and performance measurement for vector search Face Recognition

No, creating dummy unknowns is not the best way to do it.

A better approach can be, if a new face comes in, we calculate distance between vector of the new face and all of the vectors of known faces already present with us. And to identify the correct face, the minimum distance is considered. But this minimum distance should also be below a threshold value. If the minimum distance is above the threshold value, then it is considered as an unknown face. The threshold value is set manually.

Just to give you the idea, here is pseudo example -

Let's say you have 5 registered (known) faces. Their vectors are [1] [2] [3] [4] [5]. Assume that your model represents each face by a vector of shape 1 x 1.

You set the threshold value as 0.4

Your distance function is defined as abs(vector1 - vector2)

Scenario 1 -

A new face comes in, and your model generates a vector [2.2]

So you calculate the distance between new face and known faces as - [1.2] [0.2] [1.2] [2.2] [3.2]

Your minimum distance is [0.2] which is less than your threshold 0.4. Hence, this new face is identified as "[2] 2nd face" face.

Scenario 2 -

A new face comes in, and your model generates a vector [7.5]

So you calculate the distance between new face and known faces as - [6.5] [5.5] [4.5] [3.5] [2.5]

Your minimum distance is [2.5] which is more than your threshold 0.4. Hence, this new face is identified as an "unknown" face.

Lastly,

As you mentioned, you can use KNN based approach. Although it would produce very accurate results, it might not scale well. As number of faces in your database grows, KNN approach will slow down.