Using neural network for "features matching" binary classification We have a dataset of numerical features from two images and we want to check if these images match or not using only these features.
Basically we have have these columns:


fA1, fA2, ..., fA14: 14 features from image A
fB1, fB3, ..., fB14: 14 features from image B


We want to predict if image A match image B (y=1) or not (y=0). So it's a features matching problem.

The main usecase is for face recognition using this framework:
BERND HEISELE

So is there any neural networks architecture known for this situation (we have a 7 million annotated training set) ?

N.B: we don't have any images, we have only numerical features.
From what I understand, your dataset is of pairs of images and a binary classification of their pairing?

There are models using an architecture named Siamese Neural Networks which are used exactly for this task of determining similarity.

You can start by reading the following article: Learning to Compare Image Patches via Convolutional Neural Networks. They are using Siamese architecture to compare between different image patches (similar to what you are describing):