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.

翻译：极端头部姿态是面部分析任务（包括人脸检测、面部关键点定位（FLD）和头部姿态估计（HPE））中常见的挑战。这些任务相互依赖：精确的FLD依赖于鲁棒的人脸检测，而HPE又与这些关键点密切相关。本文聚焦于上述任务的集成，尤其是在应对大角度人脸姿态带来的复杂问题时。本研究的主要贡献在于提出一种能够同时实现人脸、面部关键点和头部姿态联合检测的实时多任务检测系统。该系统基于广泛采用的YOLOv8检测框架，通过增加额外的关键点回归头扩展了原有目标检测头，从而实现对关键面部关键点的高效定位。此外，我们对原始YOLOv8框架中的多个模块进行了优化与增强。为验证所提模型的有效性与实时性能，我们在300W-LP和AFLW2000-3D数据集上开展了广泛实验。实验结果证实，该模型能够应对大角度人脸姿态的挑战，并在这些相互关联的任务中实现实时性能。