Abstract
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.
Abstract (translated)
极端头姿势在面部分析任务中提出了一起常见的挑战,包括面部检测、面部地标检测(FLD)和头部姿势估计(HPE)。这些任务是相互依存的,准确的FLD依赖于强大的面部检测,而HPE与这些关键点密切相关。本文重点探讨了这些任务的整合,特别是在处理大角度面部姿势的复杂性时。本研究的主要贡献是提出了一种实时多任务检测系统,能够同时完成面部、面部地标和头部姿势的联合检测。该系统基于广泛使用的YOLOv8检测框架,通过添加地标回归头扩展了原对象检测头,从而能够高效地定位关键的面部地标。此外,我们在原YOLOv8框架中优化和改进了各种模块。为了验证我们提出的模型的有效性和实时性能,我们在300W-LP和AFLW2000-3D数据集上进行了广泛的实验。所获得的结果验证我们的模型能够应对大角度面部姿势挑战,同时在这些相互关联的任务中提供实时性能。
URL
https://arxiv.org/abs/2309.11773
