Department of Electronic Engineering, Soongsil University, Seoul 06978, Republic of Korea.
School of AI Convergence, Soongsil University, Seoul 06978, Republic of Korea.
Sensors (Basel). 2023 May 13;23(10):4731. doi: 10.3390/s23104731.
Face alignment methods have been actively studied using coordinate and heatmap regression tasks. Although these regression tasks have the same objective for facial landmark detection, each task requires different valid feature maps. Therefore, it is not easy to simultaneously train two kinds of tasks with a multi-task learning network structure. Some studies have proposed multi-task learning networks with two kinds of tasks, but they do not suggest an efficient network that can train them simultaneously because of the shared noisy feature maps. In this paper, we propose a heatmap-guided selective feature attention for robust cascaded face alignment based on multi-task learning, which improves the performance of face alignment by efficiently training coordinate regression and heatmap regression. The proposed network improves the performance of face alignment by selecting valid feature maps for heatmap and coordinate regression and using the background propagation connection for tasks. This study also uses a refinement strategy that detects global landmarks through a heatmap regression task and then localizes landmarks through cascaded coordinate regression tasks. To evaluate the proposed network, we tested it on the 300W, AFLW, COFW, and WFLW datasets and obtained results that outperformed other state-of-the-art networks.
人脸对齐方法已经通过坐标和热图回归任务得到了积极的研究。虽然这些回归任务对于面部地标检测具有相同的目标，但每个任务都需要不同的有效特征图。因此，使用多任务学习网络结构同时训练两种任务并不容易。一些研究提出了具有两种任务的多任务学习网络，但由于共享的噪声特征图，它们并没有提出一种有效的网络来同时训练它们。在本文中，我们提出了一种基于多任务学习的热图引导选择性特征注意的鲁棒级联人脸对齐方法，通过有效地训练坐标回归和热图回归，提高了人脸对齐的性能。所提出的网络通过为热图和坐标回归选择有效特征图，并为任务使用背景传播连接，提高了人脸对齐的性能。该研究还使用了一种细化策略，通过热图回归任务检测全局地标，然后通过级联坐标回归任务定位地标。为了评估所提出的网络，我们在 300W、AFLW、COFW 和 WFLW 数据集上进行了测试，结果优于其他最先进的网络。
