I'm working on developing an face authentication system using Convolution Neural Network (CNN). I know that the CNN can be used to classify two classes. However, my problem is how can I train the CNN as the training set in the face authentication system should be only for the clients users?. In other words, how can I train my CNN using only one type of data? My dataset consists of 106 persons, each one has 50 images. Is there any idea on how can I divided my dataset to be suitable for the face authentication system? your help in this regard is highly appreciated.
Assuming your face authentication system is supposed to only detect users from the client database and reject those who aren't, the best way to train the model would be to use few images of all clients in the database and then test the model on the remaining images of those clients. For example,
Number of users = 106
Number of images per user = 50
Total amount of available data = 106 x 50 = 5300 images
Training Data: 35 images of all users => 35 x 106 = 3710 images
Testing Data: 15 images of all users => 15 x 106 = 1590 images
You could go ahead and also use a validation set and cross validation to better train your model.