Nowadays, face detection and head pose estimation have a lot of application such as face recognition, aiding in gaze estimation and modeling attention. For these two tasks, it is usually to design two different models. However, the head pose estimation model often depends on the region of interest (ROI) detected in advance, which means that a serial face detector is needed. Even the lightest face detector will slow down the whole forward inference time and cannot achieve real-time performance when detecting the head pose of multiple people. We can see that both face detection and head pose estimation need face features, so a shared face feature map can be used between them. In this paper, a multi-task learning model is proposed that can solve both problems simultaneously. We directly detect the location of the center point of the bounding box of face; at this location, we calculate the size of the bounding box of face and the head attitude. We evaluate our model’s performance on the AFLW. The proposed model has great competitiveness with the multi-stage face attribute analysis model, and our model can achieve real-time performance.

如今，人脸检测和头部姿势估计有很多应用，例如人脸识别、帮助注视估计和建模注意力。对于这两个任务，通常是设计两个不同的模型。然而，头部姿态估计模型往往取决于预先检测到的感兴趣区域（ROI），这意味着需要串行人脸检测器。即使是最轻的人脸检测器也会减慢整个前向推理时间，并且在检测多个人的头部姿势时无法达到实时性能。我们可以看到人脸检测和头部姿态估计都需要人脸特征，因此它们之间可以使用共享的人脸特征图。在本文中，提出了一种可以同时解决这两个问题的多任务学习模型。我们直接检测人脸边界框中心点的位置；在这个位置，我们计算人脸和头部姿态的边界框的大小。我们评估我们的模型在 AFLW 上的表现。所提出的模型与多阶段人脸属性分析模型具有很大的竞争力，我们的模型可以实现实时性能。