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Abstract：
In this paper, we propose a learning state evaluation method based on face detection and head pose estimation. This method is suitable for mobile devices with weak computing power, so it is necessary to control the parameter quantity of the face detection and head pose estimation network. Firstly, we propose a ghost and attention module (GA) base face detection network (GA-Face). GA-Face reduces the number of parameters and computation in the feature extraction network through the ghost module, and focuses the network on important features through a parameter-free attention mechanism. We also propose a lightweight dual-branch (DB) head pose estimation network: DB-Net. Finally, we propose a student learning state evaluation algorithm. This algorithm can evaluate the learning status of students based on the distance between their faces and the screen, as well as their head posture. We validate the effectiveness of the proposed GA-Face and DB-Net on several standard face detection datasets and standard head pose estimation datasets. Finally, we validate, through practical cases, that the proposed online learning state assessment method can effectively assess the level of student attention and concentration, and, due to its low computational complexity, will not interfere with the student\'s learning process.
摘要：
在本文中,提出了一种基于人脸检测和头部姿态估计的学习状态评价方法。该方法适用于计算能力较弱的移动设备,因此有必要对人脸检测和头部姿态估计网络的参数进行控制。首先,我们提出了一个鬼影和注意力模块(GA)基本人脸检测网络(GA-Face)。GA-Face通过鬼影模块减少了特征提取网络中的参数数量和计算量，并通过无参数的注意力机制将网络集中在重要特征上。我们还提出了一个轻量级的双分支（DB）头部姿态估计网络：DB-Net。最后,提出了一种学生学习状态评价算法。该算法可以根据学生的面部与屏幕之间的距离来评估学生的学习状态，以及他们的头部姿势。我们在几个标准人脸检测数据集和标准头部姿态估计数据集上验证了所提出的GA-Face和DB-Net的有效性。最后,我们验证，通过实际案例，认为所提出的在线学习状态评估方法可以有效地评估学生的注意力和专注度，and,由于其计算复杂度低，不会干扰学生的学习过程。
