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Structure-aware Heatmap and Boundary Map Regression Based Robust Face Alignment

HUANG, L. See more information about HUANG, L. on SCOPUS See more information about HUANG, L. on IEEExplore See more information about HUANG, L. on Web of Science, WU, Y. See more information about WU, Y. on SCOPUS See more information about WU, Y. on SCOPUS See more information about WU, Y. on Web of Science
 
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Download PDF pdficon (2,608 KB) | Citation | Downloads: 894 | Views: 922

Author keywords
distance learning, image analysis, neural network, pattern analysis, supervised learning

References keywords
vision(46), face(32), alignment(26), recognition(25), pattern(23), facial(21), landmark(20), detection(18), cvpr(16), robust(11)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2023-05-31
Volume 23, Issue 2, Year 2023, On page(s): 3 - 10
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2023.02001
Web of Science Accession Number: 001009953400001
SCOPUS ID: 85164342223

Abstract
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Large head pose variations and severe occlusion are challenging problems for face alignment. In this paper, we propose a Structure-aware Heatmap and Boundary map Regression Network (SHBRN), consisting of a rough estimation network and a refinement network, to accounting for the structural geometry of faces via the boundary map. Specifically, in the rough estimation network, a structure-aware module is designed to capture low-level features rich in structure information, and both heatmaps and boundary maps are predicted by the hourglass network. In this way, the network can not only estimate the initial location of keypoints, but also implicitly take the geometric structure into consideration. In the refinement network, the boundary maps and heatmaps are fused with the features extracted in the rough stage via attention mechanism. As a result, the network can combine the global information with local appearance for obtaining complete face representations, and also optimize the spatial relationship of different keypoints. Our proposed network is superior to the existing methods on 300W, COFW, and AFLW datasets, especially for those challenging situations, which proves the effectiveness and robustness of our model.


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References Weight

Web of Science® Citations for all references: 17,876 TCR
SCOPUS® Citations for all references: 28,418 TCR

Web of Science® Average Citations per reference: 288 ACR
SCOPUS® Average Citations per reference: 458 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

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