I am classifying about 3000 thousand people's faces using FaceNet. Each person has about 100 photos.
FaceNet first calculates a face embedding ( a feature vector) for each photo. So each person has 100 face embeddings.
What I want to do is aggregate the face embedding of each person into one. What is the best way of doing this?
I have tried to use mean method. But I am not sure whether this is recommended way.
-- The reason I want this is because using a single SVM as classifier for 3000 labels is very slow. (I took 50+ hours and about 250G memory and it still won't finish training). So I need to divide the training data into subsets, and use multiple SVCs to get first level of results. Then I uses the aggregated face-embedding of each person and closest distance to get second level result.