  With the emergence of service robots and surveillance cameras, dynamic face
recognition (DFR) in wild has received much attention in recent years. Face
detection and head pose estimation are two important steps for DFR. Very often,
the pose is estimated after the face detection. However, such sequential
computations lead to higher latency. In this paper, we propose a low latency
and lightweight network for simultaneous face detection, landmark localization
and head pose estimation. Inspired by the observation that it is more
challenging to locate the facial landmarks for faces with large angles, a pose
loss is proposed to constrain the learning. Moreover, we also propose an
uncertainty multi-task loss to learn the weights of individual tasks
automatically. Another challenge is that robots often use low computational
units like ARM based computing core and we often need to use lightweight
networks instead of the heavy ones, which lead to performance drop especially
for small and hard faces. In this paper, we propose online feedback sampling to
augment the training samples across different scales, which increases the
diversity of training data automatically. Through validation in commonly used
WIDER FACE, AFLW and AFLW2000 datasets, the results show that the proposed
method achieves the state-of-the-art performance in low computational
resources. The code and data will be available at
https://github.com/lyp-deeplearning/MOS-Multi-Task-Face-Detect.
