Training an emotion detector with transfer learning

The first thing to do in any machine learning task is to collect the data. What we need are thousands of images with labeled facial expressions. The public FER dataset [1] is a great starting point with 28,709 labeled images. However, since the resolution of these images is only 48 x 48, it would be nice to also have a dataset with richer features. To do this, we will use the google_images_download python package to query and scrape data from Google Images.