Facial landmark detection via attention-adaptive deep network

Sadiq, Muhammad, Shi, Daming, Guo, Meiqin and Cheng, Xiaochun ORCID logoORCID: https://orcid.org/0000-0003-0371-9646 (2019) Facial landmark detection via attention-adaptive deep network. IEEE Access, 7 . pp. 181041-181050. ISSN 2169-3536 [Article] (doi:10.1109/ACCESS.2019.2955156)

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Facial landmark detection is a key component of the face recognition pipeline as well as facial attribute analysis and face verification. Recently convolutional neural network-based face alignment methods have achieved significant improvement, but occlusion is still a major source of a hurdle to achieve good accuracy. In this paper, we introduce the attentioned distillation module in our previous work Occlusion-adaptive Deep Network (ODN) model, to improve performance. In this model, the occlusion probability of each position in high-level features are inferred by a distillation module. It can be learnt automatically in the process of estimating the relationship between facial appearance and facial shape. The occlusion probability serves as the adaptive weight on high-level features to reduce the impact of occlusion and obtain clean feature representation. Nevertheless, the clean feature representation cannot represent the holistic face due to the missing semantic features. To obtain exhaustive and complete feature representation, it is vital that we leverage a low-rank learning module to recover lost features. Considering that facial geometric characteristics are conducive to the low-rank module to recover lost features, the role of the geometry-aware module is, to excavate geometric relationships between different facial components. The role of attentioned distillation module is, to get rich feature representation and model occlusion. To improve feature representation, we used channel-wise attention and spatial attention. Experimental results show that our method performs better than existing methods.

Item Type: Article
Keywords (uncontrolled): Facial landmarks, channel attention, spatial attention, deep learning, scalable computing
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 29521
Notes on copyright: This work is licensed under a Creative Commons Attribution 4.0 License.
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Depositing User: Xiaochun Cheng
Date Deposited: 12 Mar 2020 15:09
Last Modified: 09 Sep 2021 10:35
URI: https://eprints.mdx.ac.uk/id/eprint/29521

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