首页|Reasoning structural relation for occlusion-robust facial landmark localization
Reasoning structural relation for occlusion-robust facial landmark localization
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NSTL
Elsevier
A B S T R A C T In facial landmark localization tasks, various occlusions heavily degrade the localization accuracy due to the partial observability of facial features. This paper proposes a structural relation network (SRN) for occlusion-robust landmark localization. Unlike most existing methods that simply exploit the shape con-straint, the proposed SRN aims to capture the structural relations among different facial components. These relations can be considered a more powerful shape constraint against occlusion. To achieve this, a hierarchical structural relation module (HSRM) is designed to hierarchically reason the structural rela-tions that represent both long-and short-distance spatial dependencies. Compared with existing network architectures,the HSRM can efficiently model the spatial relations by leveraging its geometry-aware net-work architecture, which reduces the semantic ambiguity caused by occlusion. Moreover, the SRN aug -ments the training data by synthesizing occluded faces. To further extend our SRN for occluded video data, we formulate the occluded face synthesis as a Markov decision process (MDP). Specifically, it plans the movement of the dynamic occlusion based on an accumulated reward associated with the perfor-mance degradation of the pre-trained SRN. This procedure augments hard samples for robust facial land -mark tracking. Extensive experimental results indicate that the proposed method achieves outstanding performance on occluded and masked faces. Code is available at https://github.com/zhuccly/SRN (c) 2021 Elsevier Ltd. All rights reserved.
NSTL
Elsevier
