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Part 1 (2019)
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I used facial keypoint detection dataset from kaggle. (CNN+regression)

It’s a challenging problem. Mainly for two reasons.

  1. Only 1/3 of the training images have data of all the 15 facial keypoints.
  2. And, most of them are erroneous (see below).

As you can see, the training data is not very accurate.
trainind_pics

Rmse Loss

tran_val_loss_facekey

Predictions

predictions_facial_keypoint
predictions_facial_keypoint.png479×640 47.7 KB

What I learnt…

  • The dataset was created using some software, or maybe a camera/device that gives out these key points. Whatever maybe, there must be a underlying mathematical model(fn) for that (camera/device/software). So, that’s what the neural network is trying to approximate, instead of the finding the actual key points. What I mean to say is, here the neural network is not trying to find exactly where is the mouth, eyes or nose; because we haven’t explicitly mentioned it in our dataset. Yoshua Bengio and team created this dataset, I would like to know if there was any intention of such sorts.
  • if that’s the case, even if we predict the actual facial keypoints for the test set, we can expect a higher error.

Also, I have re-structured the original dataset into jpeg images. https://www.kaggle.com/sharwon/facialkeypointsdetectionimg. I think this will be helpful for beginners.

Notebook is still a work in progress. I’ll share a clean version soon.
Also, The submission file is a bit weird. I’m not sure why they are not evaluating on the basis of all the points.

To do:

  • Clean the notebook.
  • Submit to the competition.
  • Use the whole data for training.
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