  Transfer learning using pre-trained Convolutional Neural Networks (CNNs) has
been successfully applied to images for different classification tasks. In this
paper, we propose a new pipeline for pain expression recognition in neonates
using transfer learning. Specifically, we propose to exploit a pre-trained CNN
that was originally trained on a relatively similar dataset for face
recognition (VGG Face) as well as CNNs that were pre-trained on a relatively
different dataset for image classification (iVGG F,M, and S) to extract deep
features from neonates' faces. In the final stage, several supervised machine
learning classifiers are trained to classify neonates' facial expression into
pain or no pain expression. The proposed pipeline achieved, on a testing
dataset, 0.841 AUC and 90.34 accuracy, which is approx. 7 higher than the
accuracy of handcrafted traditional features. We also propose to combine deep
features with traditional features and hypothesize that the mixed features
would improve pain classification performance. Combining deep features with
traditional features achieved 92.71 accuracy and 0.948 AUC. These results show
that transfer learning, which is a faster and more practical option than
training CNN from the scratch, can be used to extract useful features for pain
expression recognition in neonates. It also shows that combining deep features
with traditional handcrafted features is a good practice to improve the
performance of pain expression recognition and possibly the performance of
similar applications.
