APSIPA Transactions on Signal and Information Processing > Vol 12 > Issue 1

Lightweight Boundary-Aware Face Alignment with Compressed HourglassNet and Transformer

Wenhui Wang, University of Jinan, China, Yingxin Li, University of Jinan, China, Ziqiang Li, University of Jinan, China, Jingliang Peng, University of Jinan, China, ise_pengjl@ujn.edu.cn
Suggested Citation
Wenhui Wang, Yingxin Li, Ziqiang Li and Jingliang Peng (2023), "Lightweight Boundary-Aware Face Alignment with Compressed HourglassNet and Transformer", APSIPA Transactions on Signal and Information Processing: Vol. 12: No. 1, e45. http://dx.doi.org/10.1561/116.00000059

Publication Date: 06 Nov 2023
© 2023 W. Wang, Y. Li, Z. Li and J. Peng
Face alignmentFacial landmark detectionLightweight


Open Access

This is published under the terms of CC BY-NC.

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In this work, we focus on lightweight and accurate face alignment. For that purpose, we propose an algorithm design that promotes a most recently published face alignment method in terms of model size and computing cost while maintaining high accuracy of face alignment. Specifically, we construct a lightweight two-stage neural network. The first stage estimates boundary heatmaps on the facial region, which are then used to guide the facial landmark position prediction in the second stage. For the first stage, we compress an HourglassNet-based structure by reducing the numbers of feature channels and convolutional kernels and optimizing the structure of Hourglass block by ShuffleNet modules. For the second stage, we compress the subnet by utilizing DeLighT, a recently published lightweight version of Transformer. Experimental results on several standard facial landmark detection datasets show that the proposed algorithm achieves sharp advances in model compactness and computing efficiency while keeping a state-of-the-art level of accuracy in facial landmark detection.