文献类型：期刊文献

英文题名：Multitask Learning-Based Facial Landmark Detection via Signal Processing: Eliminating Artificial Auxiliary Labeling for Autonomous Driving

作者：Dai, Songyin[1,2]; Zhang, Chaoran[1,2]; Xu, Cheng[1,2]; Pan, WeiGuo[1,2]

第一作者：代松银

机构：[1] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, China; [2] Institute for Brain and Cognitive Sciences, College of Robotics, Beijing Union University, Beijing, China

第一机构：北京联合大学北京市信息服务工程重点实验室

年份：2025

外文期刊名：International Journal of High Speed Electronics and Systems

收录：EI(收录号：20250817933749)

语种：英文

外文关键词：Adversarial machine learning - Contrastive Learning - Face recognition - Federated learning

摘要：This study introduces a novel facial landmark detection framework that leverages signal processing techniques to eliminate the dependency on artificial auxiliary labeling. Built upon multi-task learning, the proposed method constructs auxiliary information directly from labeled facial landmarks, including inter-landmark connection distances and facial triangle angle features. By incorporating multi-task collaborative optimization, the method enforces explicit constraints on global face shape and enhances feature representation, significantly improving detection accuracy. A tailored joint loss function integrates geometric features and addresses data distribution imbalance, ensuring robust penalty for rare samples while further constraining global face geometry. Experimental evaluations on benchmark datasets, including 300W and AFLW, demonstrate that the proposed approach outperforms the existing state-of-the-art methods in terms of accuracy and reliability. Moreover, our study reveals that efficient facial landmark detection can be achieved without artificial auxiliary annotation information through a multitask learning framework. These results establish the efficacy of the method for practical applications, such as autonomous driving systems. ? The Author(s)