期刊
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW 2021
卷 -, 期 -, 页码 1462-1471出版社
IEEE COMPUTER SOC
DOI: 10.1109/CVPRW53098.2021.00162
关键词
-
资金
- National Natural Science Foundation of China [61906195, 61906193]
This paper proposes a strong baseline method which views head pose estimation as a graph regression problem and utilizes Graph Convolutional Networks to model complex mappings. Introducing mechanisms like EVA, ACA, and DCA further enhances performance, with experiment results showing superiority over state-of-the-art landmark-based and landmark-free methods.
Head pose estimation is an important task in many real-world applications. Since the facial landmarks usually serve as the common input that is shared by multiple downstream tasks, utilizing landmarks to acquire high-precision head pose estimation is of practical value for many real-world applications. However, existing landmark-based methods have a major drawback in model expressive power, making them hard to achieve comparable performance to the landmark-free methods. In this paper, we propose a strong baseline method which views the head pose estimation as a graph regression problem. We construct a landmark-connection graph, and propose to leverage the Graph Convolutional Networks (GCN) to model the complex nonlinear mappings between the graph typologies and the head pose angles. Specifically, we design a novel GCN architecture which utilizes joint Edge-Vertex Attention (EVA) mechanism to overcome the unstable landmark detection. Moreover, we introduce the Adaptive Channel Attention (ACA) and the Densely-Connected Architecture (DCA) to boost the performance further. We evaluate the proposed method on three challenging benchmark datasets. Experiment results demonstrate that our method achieves better performance in comparison with the state-of-the-art landmark-based and landmark-free methods.
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