How to add new Category in the CNN for Attendance by AI
You don't need classification. Classification is not the solution for everywhere problem.
You should look into these: 

Cosine Similarity
Siamese Network

You can use existing models from FaceNet or OpenCV. 
Since they are already trained on a huge dataset of faces, you can extract feature vector easily. 
Store the feature vector for every new student. 
Then compute similarities(existing image, current image) based on distance or similarity score mark attendance.
This is scable and much faster approach. No training or retraining.
I am working on the project with the group and we have decided to make the project on the ' Automatic Attendance System by AI '
I have learned the CNNs to categorize the objects i.e dogs and cats.
With that knowledge, we have decided to make the attendance system based on CNN. ( Please tell me if we shouldn't take this path or the technology if you find something bad here... )
But continuing it with CNN, let's say we have trained the model with 2 students, and on the last layer we put the two neurons as they are just two, right...?
Now the third comes, now to train his face to the NN, I have to change the model's structure and retrain every faces again...
If we apply the project to the big institute, where hundreds of students are there and if we want to train the model for each individual student, the nthis is not the feasible solution to recreate the model..
So we thought, we will fix the model's output layer size to let's say 50.
So only 50 faces can be trained per model.
But it is not always possible that there will always be 50.
They may 40 or if one gets in with ne admission, then 41.
So how to re-train the network with existing weights ?
( The same question is asked somewhere I know, but please direct me with my situation )

Or is there any other technology to use... ?

Please Direct me...