  While convolutional neural networks (CNNs) have significantly boosted the
performance of face related algorithms, maintaining accuracy and efficiency
simultaneously in practical use remains challenging. Recent study shows that
using a cascade of hourglass modules which consist of a number of bottom-up and
top-down convolutional layers can extract facial structural information for
face alignment to improve accuracy. However, previous studies have shown that
features produced by shallow convolutional layers are highly correspond to
edges. These features could be directly used to provide the structural
information without addition cost. Motivated by this intuition, we propose an
efficient multitask face alignment, face tracking and head pose estimation
network (ATPN). Specifically, we introduce a shortcut connection between
shallow-layer features and deep-layer features to provide the structural
information for face alignment and apply the CoordConv to the last few layers
to provide coordinate information. The predicted facial landmarks enable us to
generate a cheap heatmap which contains both geometric and appearance
information for head pose estimation and it also provides attention clues for
face tracking. Moreover, the face tracking task saves us the face detection
procedure for each frame, which is significant to boost performance for
video-based tasks. The proposed framework is evaluated on four benchmark
datasets, WFLW, 300VW, WIDER Face and 300W-LP. The experimental results show
that the ATPN achieves improved performance compared to previous
state-of-the-art methods while having less number of parameters and FLOPS.
