Appending user training images when needed

[SaddamBInSyed]

Hi @alessiosavi
Thanks for your work.

I have 10 users that need to be enrolled in order to recognize them later.
but I got only the 5 person faces and I zipped those and trained the same.

So Is it possible to add the remaining 5 more users to the existing already trained file. (pickle/dataset)?

please advise.

[ClashLuke]

In your particular case the model is prone to overfit, so it might not be a good idea to add new cases on the fly (like you'd do in online learning).
Is it possible for you to first acquire one image of each class (in this case user) and then add more as you get more?

[SaddamBInSyed]

Thanks for your reply.

"Is it possible for you to first acquire one image of each class (in this case user) and then add more as you get more?"

No, actually the person/employee is in leave/absent when we collecting/enrolling all employee/user faces.
Once the employee is back to the office then again we will go and collect the same.

So now we waiting to collect all the person images to train in one go.
but I want to know How to (add/remove) train the new faces on the fly.

One more scenario:

If tomorrow some trained employee/user is terminated/resigned from the company then we have to remove his face from the dataset/pickle file as well.

please advise

[ClashLuke]

Those are two very interesting scenarios.
The first one you mentioned, adding new training data, should be possible. If @alessiosavi can't add the feature right now I'll release a pull request by Sunday.

However, it comes with another challenge as well. If you have more people you want to detect, you also have more different classes.
Unfortunately just adding more classes isn't really possible, unless we choose to use PyTorch (#2).
Back to your problem: For now you have to provision how many different people you want to predict at a given time.

The second one, removing from the dataset and the predictions, is split in two parts as well.
Removing them from the dataset is possible to be implemented right now. The second is blocked by issue #2 as well.

I hope that clears it a bit up and potentially solves your issue.

[SaddamBInSyed]

"However, it comes with another challenge as well. If you have more people you want to detect, you also have more different classes.
Unfortunately just adding more classes isn't really possible, unless we choose to use PyTorch (#2)."

I am trying to understand the above statement but no luck.

Are you trying to say, we can not add more userimages to dataset (e;g adding 10K employee images and train them)

also why we need PyTorch here
please clarify?

[ClashLuke]

If you tell the neural network to classify between ten people that works fine. Even if you only give it two people the first day and over time add new employees.
However, as it puts all the faces in "buckets", and you only have ten buckets, you can't really tell it that someone goes into the 11th bucket, as there is no such thing.

That's where PyTorch comes into play. With PyTorch you can just create the 11th bucket or remove the 3rd.
Got a new employee? Add them. One got sick? Remove them.

[SaddamBInSyed]

Thanks for your reply.

I dont think I understood.
Here my view,

Total user to enroll = 100
so each folder( named as username_id) has at least 1 image.
Now
day 1 I trained the NN with available data and got the pickle file.

day 2 I again train the new user data and now I have second pickle file.

Now during KNN/MLP.train() process if I combine the above 2 pickle file ( contains dlib face encodings of all user images) then now my NN has knowledge of new users as well.

now I can call my new unknown image with predict function.

Am I right? if not where I am blank.

please advise.

[ClashLuke]

You're right, if you first create enough user_id's and then assign images to each that should work!
However, you still can't predict more than 100 users and there might be a slight chance that a terminated employee is still falsely recognized. This should be fixed with #25.

I think I don't quite understand your pipeline though. Why do you want to combine the two pickle files? Doesn't the second already contain the images of the first?

[SaddamBInSyed]

Doesn't the second already contain the images of the first?
o. 2 files are different ( 1st file = 1to50 user image and 2nd file = 51 to 100 user image).

[ClashLuke]

Then what stops you from generating a 2nd file with 1 to 100?

Regarding your point, pickles are binary files, so appending them may be difficult. However, we could create a new issue regarding the concatenation of datasets.

[SaddamBInSyed]

yes. it would be nice if we have provision to concatenate multiple pickle files.

thank you.