  Current image description generation models do not transfer well to the task
of describing human faces. To encourage the development of more human-focused
descriptions, we developed a new data set of facial descriptions based on the
CelebA image data set. We describe the properties of this data set, and present
results from a face description generator trained on it, which explores the
feasibility of using transfer learning from VGGFace/ResNet CNNs. Comparisons
are drawn through both automated metrics and human evaluation by 76
English-speaking participants. The descriptions generated by the VGGFace-LSTM +
Attention model are closest to the ground truth according to human evaluation
whilst the ResNet-LSTM + Attention model obtained the highest CIDEr and CIDEr-D
results (1.252 and 0.686 respectively). Together, the new data set and these
experimental results provide data and baselines for future work in this area.
