Collaborative learning network for head pose estimation

doi:10.1016/j.imavis.2022.104555

Collaborative learning network for head pose estimation

2022-11-01

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DOI：10.1016/j.imavis.2022.104555

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Head pose estimation is an important task in many real-world applications, such as human-computer interac-tion, driver monitoring, face localization and gaze estimation. In this paper, we present a novel collaborative learning framework based on Convolutional Neural Networks (CNNs) for head pose estimation. The proposed framework consists of a landmark-based branch and a landmark-free branch. The former first estimates facial landmarks and then follows the Landmark-MLP-Mixer module which models the complex nonlinear mapping relationship from facial landmarks to head pose angles. While the later adopts a label distribution learning strategy to estimate head pose. The two branches both dedicate themselves to head pose estimation task, and they collaborate with each other for mutual promotion and complementary semantic learning. Specifically, we introduce a dual-branch transfer module in the middle of the network to achieve explicit semantic interaction and introduce a multi-loss strategy that induces to implicit information interaction. We conduct extensive exper-iments on several popular benchmarks, including AFLW, AFLW2000 and BIWI, the results show that our method is competitive compared to other state-of-the-art methods. (c) 2022 Elsevier B.V. All rights reserved.

Keywords:

Head pose estimation

Collaborative learning

Convolutional neural networks

Dual-branch transfer

Multi -loss strategy

Collaborative learning network for head pose estimation

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