  This paper presents a classifier ensemble for Facial Expression Recognition
(FER) based on models derived from transfer learning. The main experimentation
work is conducted for facial action unit detection using feature extraction and
fine-tuning convolutional neural networks (CNNs). Several classifiers for
extracted CNN codes such as Linear Discriminant Analysis (LDA), Support Vector
Machines (SVMs) and Long Short-Term Memory (LSTM) are compared and evaluated.
Multi-model ensembles are also used to further improve the performance. We have
found that VGG-Face and ResNet are the relatively optimal pre-trained models
for action unit recognition using feature extraction and the ensemble of
VGG-Net variants and ResNet achieves the best result.
