  Inspired by the recent development of deep network-based methods in semantic
image segmentation, we introduce an end-to-end trainable model for face mask
extraction in video sequence. Comparing to landmark-based sparse face shape
representation, our method can produce the segmentation masks of individual
facial components, which can better reflect their detailed shape variations. By
integrating Convolutional LSTM (ConvLSTM) algorithm with Fully Convolutional
Networks (FCN), our new ConvLSTM-FCN model works on a per-sequence basis and
takes advantage of the temporal correlation in video clips. In addition, we
also propose a novel loss function, called Segmentation Loss, to directly
optimise the Intersection over Union (IoU) performances. In practice, to
further increase segmentation accuracy, one primary model and two additional
models were trained to focus on the face, eyes, and mouth regions,
respectively. Our experiment shows the proposed method has achieved a 16.99%
relative improvement (from 54.50% to 63.76% mean IoU) over the baseline FCN
model on the 300 Videos in the Wild (300VW) dataset.
