A Real-Time Multi-Task Learning System for Joint Detection of Face,
  Facial Landmark and Head Pose
Qingtian Wu Liming Zhang
Extreme head postures pose a common challenge across a spectrum of facial
analysis tasks, including face detection, facial landmark detection (FLD), and
head pose estimation (HPE). These tasks are interdependent, where accurate FLD
relies on robust face detection, and HPE is intricately associated with these
key points. This paper focuses on the integration of these tasks, particularly
when addressing the complexities posed by large-angle face poses. The primary
contribution of this study is the proposal of a real-time multi-task detection
system capable of simultaneously performing joint detection of faces, facial
landmarks, and head poses. This system builds upon the widely adopted YOLOv8
detection framework. It extends the original object detection head by
incorporating additional landmark regression head, enabling efficient
localization of crucial facial landmarks. Furthermore, we conduct optimizations
and enhancements on various modules within the original YOLOv8 framework. To
validate the effectiveness and real-time performance of our proposed model, we
conduct extensive experiments on 300W-LP and AFLW2000-3D datasets. The results
obtained verify the capability of our model to tackle large-angle face pose
challenges while delivering real-time performance across these interconnected
tasks.