  Most of the existing work on automatic facial expression analysis focuses on
discrete emotion recognition, or facial action unit detection. However, facial
expressions do not always fall neatly into pre-defined semantic categories.
Also, the similarity between expressions measured in the action unit space need
not correspond to how humans perceive expression similarity. Different from
previous work, our goal is to describe facial expressions in a continuous
fashion using a compact embedding space that mimics human visual preferences.
To achieve this goal, we collect a large-scale faces-in-the-wild dataset with
human annotations in the form: Expressions A and B are visually more similar
when compared to expression C, and use this dataset to train a neural network
that produces a compact (16-dimensional) expression embedding. We
experimentally demonstrate that the learned embedding can be successfully used
for various applications such as expression retrieval, photo album
summarization, and emotion recognition. We also show that the embedding learned
using the proposed dataset performs better than several other embeddings
learned using existing emotion or action unit datasets.
