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With the emergence of service robots and surveillance cameras, dynamic facerecognition (DFR) in wild has received much attention in recent years. Facedetection and head pose estimation are two important steps for DFR. Very often,the pose is estimated after the face detection. However, such sequentialcomputations lead to higher latency. In this paper, we propose a low latencyand lightweight network for simultaneous face detection, landmark localizationand head pose estimation. Inspired by the observation that it is morechallenging to locate the facial landmarks for faces with large angles, a poseloss is proposed to constrain the learning. Moreover, we also propose anuncertainty multi-task loss to learn the weights of individual tasksautomatically. Another challenge is that robots often use low computationalunits like ARM based computing core and we often need to use lightweightnetworks instead of the heavy ones, which lead to performance drop especiallyfor small and hard faces. In this paper, we propose online feedback sampling toaugment the training samples across different scales, which increases thediversity of training data automatically. Through validation in commonly usedWIDER FACE, AFLW and AFLW2000 datasets, the results show that the proposedmethod achieves the state-of-the-art performance in low computationalresources. The code and data will be available athttps://github.com/lyp-deeplearning/MOS-Multi-Task-Face-Detect.

MOS: A Low Latency and Lightweight Framework for Face Detection, Landmark Localization, and Head Pose Estimation