文献类型：期刊文献

英文题名：FPTR: Facial Pose Estimation Transformer Without Keypoint Detection

作者：Chen, Hongqian[1]; Xiong, Jiahui[1]; Li, Xing[2]; Li, Hui[3]; Chen, Yi[1]

第一作者：Chen, Hongqian

机构：[1] School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing, 100048, China; [2] Department of Intelligent Manufacturing, Shandong Labor Vocational and Technical College, Jinan, 250300, China; [3] Management College, Beijing Union University, Beijing, 100101, China

第一机构：School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing, 100048, China

年份：2026

卷号：2800 CCIS

起止页码：111-126

外文期刊名：Communications in Computer and Information Science

收录：EI(收录号：20260419953761)

语种：英文

外文关键词：E-learning - Face recognition - Students - Teaching

摘要：Facial pose estimation is a very important requirement in analyzing the state of students in classrooms. To improve the accuracy of facial pose estimation in classroom scenarios, this paper proposes a network model for facial pose estimation based on Transformer, FPTR. The model directly estimates the 6DoF in an end-to-end manner and generates the facial bounding box without the need for face keypoint detection. The model is used for facial pose estimation of students in an online classroom and achieves test results that meet expectations. The application experiment of the actual classroom scene show that the method can effectively solve the problem of large face pose angle deviation predicted by the classical direct regression pose estimation method. To evaluate the effectiveness and performance of the proposed FPTR network model, multiple sets of analysis experiments including comparative analysis experiments and ablation analysis experiments are set up and analyzed. The experimental results show that the FPTR network model is capable of end-to-end facial pose estimation, directly outputs the 6DoF of a given input image without a pose adjustment step, and generates a facial bounding box. ? The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.