Region-Aware Deep Feature-Fused Network for Robust Facial Landmark Localization

Xuxin Lin and Yanyan Liang ()
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Xuxin Lin: Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China
Yanyan Liang: Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China

Mathematics, 2023, vol. 11, issue 19, 1-14

Abstract: In facial landmark localization, facial region initialization usually plays an important role in guiding the model to learn critical face features. Most facial landmark detectors assume a well-cropped face as input and may underperform in real applications if the input is unexpected. To alleviate this problem, we present a region-aware deep feature-fused network (RDFN). The RDFN consists of a region detection subnetwork and a region-wise landmark localization subnetwork to explicitly solve the input initialization problem and derive the landmark score maps, respectively. To exploit the association between tasks, we develop a cross-task feature fusion scheme to extract multi-semantic region features while trading off their importance in different dimensions via global channel attention and global spatial attention. Furthermore, we design a within-task feature fusion scheme to capture the multi-scale context and improve the gradient flow for the landmark localization subnetwork. At the inference stage, a location reweighting strategy is employed to transform the score maps into 2D landmark coordinates. Extensive experimental results demonstrate that our method has competitive performance compared to recent state-of-the-art methods, achieving NMEs of 3.28%, 1.48%, and 3.43% on the 300W, AFLW, and COFW datasets, respectively.

Keywords: facial landmark localization; face alignment; region-based CNN; deep feature fusion (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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