Robust facial landmark detection network based on multiscale attention residual blocks

Lamei Zou; Jia Xie; Hao Wang; Luhan Lu; Chengqing Wu; Yichun Guo; Tianbao Zhang

doi:10.1117/12.2688423

7 March 2024 Robust facial landmark detection network based on multiscale attention residual blocks

Lamei Zou, Jia Xie, Hao Wang, Luhan Lu, Chengqing Wu, Yichun Guo, Tianbao Zhang

Proceedings Volume 13086, MIPPR 2023: Pattern Recognition and Computer Vision; 1308603 (2024) https://doi.org/10.1117/12.2688423
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China

Abstract

We present a robust facial landmark detection network based on multiscale attention residual blocks (MARBNet) for effectively predicting facial landmark. MARBNet consists of three modules. Firstly, the coarse feature extraction module obtains coarse features through convolution, batch normalization, ReLU activation, and maximum pooling. The fine feature extraction module is composed of 33 multiscale attention residual blocks (MARB). MARB is composed of 1x1 convolution layer, 3x3 convolution layer, 1x1 convolution layer, two multiscale convolution module(MulRes) and channel attention module(CAM). MulRes is used to extract complementary features of different scales, obtain more feature information under different Receptive field, and avoid excessive loss of key information in the input image. CAM enables the network to pay more attention to high-frequency information on the channel, effectively prevents the loss of information, so as to improve the effect of facial landmark detection. The output module consists of two 1x1 convolution layers, one of which outputs landmark heatmap score and landmark coordinate offset, and the other outputs the nearest neighbor landmark offset. The experiment results on WFLW and 300W datasets show that our method is superior to the existing algorithms in terms of normalized mean square error indicators.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lamei Zou, Jia Xie, Hao Wang, Luhan Lu, Chengqing Wu, Yichun Guo, and Tianbao Zhang "Robust facial landmark detection network based on multiscale attention residual blocks", Proc. SPIE 13086, MIPPR 2023: Pattern Recognition and Computer Vision, 1308603 (7 March 2024); https://doi.org/10.1117/12.2688423

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KEYWORDS

Content addressable memory

Convolution

Feature extraction

Performance modeling

Detection and tracking algorithms

Drug discovery

Ablation

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